hypothesis for advertising

How to write a hypothesis for marketing experimentation

Creating your strongest marketing hypothesis

The potential for your marketing improvement depends on the strength of your testing hypotheses.

But where are you getting your test ideas from? Have you been scouring competitor sites, or perhaps pulling from previous designs on your site? The web is full of ideas and you’re full of ideas – there is no shortage of inspiration, that’s for sure.

Coming up with something you  want  to test isn’t hard to do. Coming up with something you  should  test can be hard to do.

Hard – yes. Impossible? No. Which is good news, because if you can’t create hypotheses for things that should be tested, your test results won’t mean mean much, and you probably shouldn’t be spending your time testing.

Taking the time to write your hypotheses correctly will help you structure your ideas, get better results, and avoid wasting traffic on poor test designs.

With this post, we’re getting advanced with marketing hypotheses, showing you how to write and structure your hypotheses to gain both business results and marketing insights!

By the time you finish reading, you’ll be able to:

• Distinguish a solid hypothesis from a time-waster, and
• Structure your solid hypothesis to get results  and  insights

To make this whole experience a bit more tangible, let’s track a sample idea from…well…idea to hypothesis.

Let’s say you identified a call-to-action (CTA)* while browsing the web, and you were inspired to test something similar on your own lead generation landing page. You think it might work for your users! Your idea is:

“My page needs a new CTA.”

*A call-to-action is the point where you, as a marketer, ask your prospect to do something on your page. It often includes a button or link to an action like “Buy”, “Sign up”, or “Request a quote”.

The basics: The correct marketing hypothesis format

A well-structured hypothesis provides insights whether it is proved, disproved, or results are inconclusive.

You should never phrase a marketing hypothesis as a question. It should be written as a statement that can be rejected or confirmed.

Further, it should be a statement geared toward revealing insights – with this in mind, it helps to imagine each statement followed by a  reason :

• Changing _______ into ______ will increase [conversion goal], because:
• Changing _______ into ______ will decrease [conversion goal], because:
• Changing _______ into ______ will not affect [conversion goal], because:

Each of the above sentences ends with ‘because’ to set the expectation that there will be an explanation behind the results of whatever you’re testing.

It’s important to remember to plan ahead when you create a test, and think about explaining why the test turned out the way it did when the results come in.

Level up: Moving from a good to great hypothesis

Understanding what makes an idea worth testing is necessary for your optimization team.

If your tests are based on random ideas you googled or were suggested by a consultant, your testing process still has its training wheels on. Great hypotheses aren’t random. They’re based on rationale and aim for learning.

Hypotheses should be based on themes and analysis that show potential conversion barriers.

At Conversion, we call this investigation phase the “Explore Phase” where we use frameworks like the LIFT Model to understand the prospect’s unique perspective. (You can read more on the the full optimization process here).

A well-founded marketing hypothesis should also provide you with new, testable clues about your users regardless of whether or not the test wins, loses or yields inconclusive results.

These new insights should inform future testing: a solid hypothesis can help you quickly separate worthwhile ideas from the rest when planning follow-up tests.

“Ultimately, what matters most is that you have a hypothesis going into each experiment and you design each experiment to address that hypothesis.” – Nick So, VP of Delivery

Here’s a quick tip :

If you’re about to run a test that isn’t going to tell you anything new about your users and their motivations, it’s probably not worth investing your time in.

Let’s take this opportunity to refer back to your original idea:

Ok, but  what now ? To get actionable insights from ‘a new CTA’, you need to know why it behaved the way it did. You need to ask the right question.

To test the waters, maybe you changed the copy of the CTA button on your lead generation form from “Submit” to “Send demo request”. If this change leads to an increase in conversions, it could mean that your users require more clarity about what their information is being used for.

That’s a potential insight.

Based on this insight, you could follow up with another test that adds copy around the CTA about next steps: what the user should anticipate after they have submitted their information.

For example, will they be speaking to a specialist via email? Will something be waiting for them the next time they visit your site? You can test providing more information, and see if your users are interested in knowing it!

That’s the cool thing about a good hypothesis: the results of the test, while important (of course) aren’t the only component driving your future test ideas. The insights gleaned lead to further hypotheses and insights in a virtuous cycle.

It’s based on a science

The term “hypothesis” probably isn’t foreign to you. In fact, it may bring up memories of grade-school science class; it’s a critical part of the  scientific method .

The scientific method in testing follows a systematic routine that sets ideation up to predict the results of experiments via:

• Collecting data and information through observation
• Creating tentative descriptions of what is being observed
• Forming  hypotheses  that predict different outcomes based on these observations
• Testing your  hypotheses
• Analyzing the data, drawing conclusions and insights from the results

Don’t worry! Hypothesizing may seem ‘sciency’, but it doesn’t have to be complicated in practice.

Hypothesizing simply helps ensure the results from your tests are quantifiable, and is necessary if you want to understand how the results reflect the change made in your test.

A strong marketing hypothesis allows testers to use a structured approach in order to discover what works, why it works, how it works, where it works, and who it works on.

“My page needs a new CTA.” Is this idea in its current state clear enough to help you understand what works? Maybe. Why it works? No. Where it works? Maybe. Who it works on? No.

Your idea needs refining.

Let’s pull back and take a broader look at the lead generation landing page we want to test.

Imagine the situation: you’ve been diligent in your data collection and you notice several recurrences of Clarity pain points – meaning that there are many unclear instances throughout the page’s messaging.

Rather than focusing on the CTA right off the bat, it may be more beneficial to deal with the bigger clarity issue.

Now you’re starting to think about solving your prospects conversion barriers rather than just testing random ideas!

If you believe the overall page is unclear, your overarching theme of inquiry might be positioned as:

• “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

By testing a hypothesis that supports this clarity theme, you can gain confidence in the validity of it as an actionable marketing insight over time.

If the test results are negative : It may not be worth investigating this motivational barrier any further on this page. In this case, you could return to the data and look at the other motivational barriers that might be affecting user behavior.

If the test results are positive : You might want to continue to refine the clarity of the page’s message with further testing.

Typically, a test will start with a broad idea — you identify the changes to make, predict how those changes will impact your conversion goal, and write it out as a broad theme as shown above. Then, repeated tests aimed at that theme will confirm or undermine the strength of the underlying insight.

Building marketing hypotheses to create insights

You believe you’ve identified an overall problem on your landing page (there’s a problem with clarity). Now you want to understand how individual elements contribute to the problem, and the effect these individual elements have on your users.

It’s game time  – now you can start designing a hypothesis that will generate insights.

You believe your users need more clarity. You’re ready to dig deeper to find out if that’s true!

If a specific question needs answering, you should structure your test to make a single change. This isolation might ask: “What element are users most sensitive to when it comes to the lack of clarity?” and “What changes do I believe will support increasing clarity?”

At this point, you’ll want to boil down your overarching theme…

• Improving the clarity of the page will reduce confusion and improve [conversion goal].

…into a quantifiable hypothesis that isolates key sections:

• Changing the wording of this CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.

Does this answer what works? Yes: changing the wording on your CTA.

Does this answer why it works? Yes: reducing confusion about the next steps in the funnel.

Does this answer where it works? Yes: on this page, before the user enters this theoretical funnel.

Does this answer who it works on? No, this question demands another isolation. You might structure your hypothesis more like this:

• Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion  for visitors coming from my email campaign  about the next steps in the funnel and improve order completions.

Now we’ve got a clear hypothesis. And one worth testing!

What makes a great hypothesis?

1. It’s testable.

2. It addresses conversion barriers.

3. It aims at gaining marketing insights.

Let’s compare:

The original idea : “My page needs a new CTA.”

Following the hypothesis structure : “A new CTA on my page will increase [conversion goal]”

The first test implied a problem with clarity, provides a potential theme : “Improving the clarity of the page will reduce confusion and improve [conversion goal].”

The potential clarity theme leads to a new hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion about the next steps in the funnel and improve order completions.”

Final refined hypothesis : “Changing the wording of the CTA to set expectations for users (from “submit” to “send demo request”) will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions.”

Which test would you rather your team invest in?

Before you start your next test, take the time to do a proper analysis of the page you want to focus on. Do preliminary testing to define bigger issues, and use that information to refine and pinpoint your marketing hypothesis to give you forward-looking insights.

Doing this will help you avoid time-wasting tests, and enable you to start getting some insights for your team to keep testing!

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How to Conduct the Perfect Marketing Experiment [+ Examples]

Updated: January 11, 2022

Published: August 30, 2021

After months of hard work, multiple coffee runs, and navigation of the latest industry changes, you've finally finished your next big marketing campaign.

Complete with social media posts, PPC ads, and a sparkly new logo, it's the campaign of a lifetime.

But how do you know it will be effective?

While there's no sure way to know if your campaign will turn heads, there is a way to gauge whether those new aspects of your strategy will be effective.

If you want to know if certain components of your campaign are worth the effort, consider conducting a marketing experiment.

Marketing experiments give you a projection of how well marketing methods will perform before you implement them. Keep reading to learn how to conduct an experiment and discover the types of experiments you can run.

What are marketing experiments?

A marketing experiment is a form of market research in which your goal is to discover new strategies for future campaigns or validate existing ones.

For instance, a marketing team might create and send emails to a small segment of their readership to gauge engagement rates, before adding them to a campaign.

It's important to note that a marketing experiment isn't synonymous with a marketing test. Marketing experiments are done for discovery, while a test confirms theories.

Why should you run a marketing experiment?

Think of running a marketing experiment as taking out an insurance policy on future marketing efforts. It’s a way to minimize your risk and ensure that your efforts are in line with your desired results.

Imagine spending hours searching for the perfect gift. You think you’ve found the right one, only to realize later that it doesn’t align with your recipient’s taste or interests. Gifts come with receipts but there’s no money-back guarantee when it comes to marketing campaigns.

An experiment will help you better understand your audience, which in turn will enable you to optimize your strategy for a stronger performance.

How to Conduct Marketing Experiments

• Brainstorm and prioritize experiment ideas.
• Find one idea to focus on.
• Make a hypothesis.
• Collect research.
• Select your metrics.
• Execute the experiment.
• Analyze the results.

Performing a marketing experiment involves doing research, structuring the experiment, and analyzing the results. Let's go through the seven steps necessary to conduct a marketing experiment.

1. Brainstorm and prioritize experiment ideas.

The first thing you should do when running a marketing experiment is start with a list of ideas.

Don’t know where to start? Look at your current priorities. What goals are you focusing on for the next quarter or the next year?

From there, analyze historical data. Were your past strategies worked in the past and what were your low performers?

As you dig into your data, you may find that you still have unanswered questions about which strategies may be most effective. From there, you can identify potential reasons behind low performance and start brainstorming some ideas for future experiments.

Then, you can rank your ideas by relevance, timeliness, and return on investment so that you know which ones to tackle first.

Keep a log of your ideas online, like Google Sheets , for easy access and collaboration.

2. Find one idea to focus on.

Now that you have a log of ideas, you can pick one to focus on.

Ideally, you organize your list based on current priorities. As such, as the business evolves, your priorities may change and affect how you rank your ideas.

Say you want to increase your subscriber count by 1,000 over the next quarter. You’re several weeks away from the start of the quarter and after looking through your data, you notice that users don’t convert once they land on your landing page.

Your landing page would be a great place to start your experiment. It’s relevant to your current goals and will yield a large return on your investment.

Even unsuccessful experiments, meaning those that do not yield expected results, are incredibly valuable as they help you to better understand your audience.

3. Make a hypothesis.

Hypotheses aren't just for science projects. When conducting a marketing experiment, the first step is to make a hypothesis you're curious to test.

A good hypothesis for your landing page can be any of the following:

• Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%.
• Removing the phone number field from the landing page form will increase the form completion rate by 25%.
• Adding a security badge on the landing page will increase the conversion rate by 10%.

This is a good hypothesis because you can prove or disprove it, it isn't subjective, and has a clear measurement of achievement.

A not-so-good hypothesis will tackle several elements at once, be unspecific and difficult to measure. For example: "By updating the photos, CTA, and copy on the landing page, we should get more sign-ups.

Here’s why this doesn’t work: Testing several variables at once is a no-go when it comes to experimenting because it will be unclear which change(s) impacted the results. The hypothesis also doesn’t mention how the elements would be changed nor what would constitute a win.

Formulating a hypothesis takes some practice, but it’s the key to building a robust experiment.

4. Collect research.

After creating your hypothesis, begin to gather research. Doing this will give you background knowledge about experiments that have already been conducted and get an idea of possible outcomes.

Researching your experiment can help you modify your hypothesis if needed.

Say your hypothesis is, "Changing the CTA copy from "Get Started" to "Join Our Community" will increase sign-ups by 5%." You may conduct more market research to validate your ideas surrounding your user persona and if they will resonate better with a community-focused approach.

It would be helpful to look at your competitors’ landing pages and see which strategies they’re using during your research.

5. Select your metrics.

Once you've collected the research, you can choose which avenue you will take and what metrics to measure.

For instance, if you’re running an email subject line experiment, the open rate is the right metric to track.

For a landing page, you’ll likely be tracking the number of submissions during the testing period. If you’re experimenting on a blog, you might focus on the average time on page.

It all depends on what you’re tracking and the question you want to answer with your experiment.

6. Execute the experiment.

Now it's time to create and perform the experiment.

Depending on what you’re testing, this may be a cross-functional project that requires collaborating with other teams.

For instance, if you’re testing a new landing page CTA, you’ll likely need a copywriter or UX writer.

Everyone involved in this experiment should know:

• The hypothesis and goal of the experiment
• The timeline and duration
• The metrics you’ll track

7. Analyze the results.

Once you've run the experiment, collect and analyze the results.

You want to gather enough data for statistical significance .

Use the metrics you've decided upon in the second step and conclude if your hypothesis was correct or not.

The prime indicators for success will be the metrics you chose to focus on.

For instance, for the landing page example, did sign-ups increase as a result of the new copy? If the conversion rate met or went above the goal, the experiment would be considered successful and one you should implement.

If it’s unsuccessful, your team should discuss the potential reasons why and go back to the drawing board. This experiment may spark ideas of new elements to test.

Now that you know how to conduct a marketing experiment, let's go over a few different ways to run them.

Marketing Experiment Examples

There are many types of marketing experiments you can conduct with your team. These tests will help you determine how aspects of your campaign will perform before you roll out the campaign as a whole.

A/B testing is one of the popular ways to marketing in which two versions of a webpage, email, or social post are presented to an audience (randomly divided in half). This test determines which version performs better with your audience.

This method is useful because you can better understand the preferences of users who will be using your product.

Find below the types of experiments you can run.

Your website is arguably your most important digital asset. As such, you’ll want to make sure it’s performing well.

If your bounce rate is high, the average time on page is low, or your visitors aren’t navigating your site in the way you’d like, it may be time to run an experiment.

2. Landing Pages

Landing pages are used to convert visitors into leads. If your landing page is underperforming, running an experiment can yield high returns.

The great thing about running a test on a landing page is that there are typically only a few elements to test: your background image, your copy, form, and CTA .

Experimenting with different CTAs can improve the number of people who engage with your content.

For instance, instead of using "Buy Now!" to pull customers in, why not try, "Learn more."

You can also test different colors of CTAs as opposed to the copy.

4. Paid Media Campaigns

There are so many different ways to experiment with ads.

Not only can you test ads on various platforms to see which ones reach your audience the best, but you can also experiment with the type of ad you create.

As a big purveyor of GIFs in the workplace, animating ads are a great way to catch the attention of potential customers. Those may work great for your brand.

You may also find that short videos or static images work better.

Additionally, you might run different types of copy with your ads to see which language compels your audience to click.

To maximize your return on ad spend (ROAS), run experiments on your paid media campaigns.

4. Social Media Platforms

Is there a social media site you're not using? For instance, lifestyle brands might prioritize Twitter and Instagram, but implementing Pinterest opens the door for an untapped audience.

You might consider testing which hashtags or visuals you use on certain social media sites to see how well they perform.

The more you use certain social platforms, the more iterations you can create based on what your audience responds to.

You might even use your social media analytics to determine which countries or regions you should focus on — for instance, my Twitter Analytics , below, demonstrates where most of my audience resides.

If alternatively, I saw most of my audience came from India, I might need to alter my social strategy to ensure I catered to India's time zone.

When experimenting with different time zones, consider making content specific to the audience you're trying to reach.

Your copy — the text used in marketing campaigns to persuade, inform, or entertain an audience — can make or break your marketing strategy .

If you’re not in touch with your audience, your message may not resonate. Perhaps you haven’t fleshed out your user persona or you’ve conducted limited research.

As such, it may be helpful to test what tone and concepts your audience enjoys. A/B testing is a great way to do this, you can also run surveys and focus groups to better understand your audience.

Email marketing continues to be one of the best digital channels to grow and nurture your leads.

If you have low open or high unsubscribe rates, it's worth running experiments to see what your audience will respond best to.

Perhaps your subject lines are too impersonal or unspecific. Or the content in your email is too long.

By playing around with various elements in your email, you can figure out the right strategy to reach your audience.

Ultimately, marketing experiments are a cost-effective way to get a picture of how new content ideas will work in your next campaign, which is critical for ensuring you continue to delight your audience.

Editor's Note: This post was originally published in December 2019 and has been updated for comprehensiveness.

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From Hypothesis to Results: Mastering the Art of Marketing Experiments

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Suppose you’re trying to convince your friend to watch your favorite movie. You could either tell them about the intriguing plot or show them the exciting trailer.

To find out which approach works best, you try both methods with different friends and see which one gets more people to watch the movie.

Marketing experiments work in much the same way, allowing businesses to test different marketing strategies, gather feedback from their target audience, and make data-driven decisions that lead to improved outcomes and growth.

By testing different approaches and measuring their outcomes, companies can identify what works best for their unique target audience and adapt their marketing strategies accordingly. This leads to more efficient use of marketing resources and results in higher conversion rates, increased customer satisfaction, and, ultimately, business growth.

Marketing experiments are the backbone of building an organization’s culture of learning and curiosity, encouraging employees to think outside the box and challenge the status quo.

In this article, we will delve into the fundamentals of marketing experiments, discussing their key elements and various types. By the end, you’ll be in a position to start running these tests and securing better marketing campaigns with explosive results.

Why Digital Marketing Experiments Matter

One of the most effective ways to drive growth and optimize marketing strategies is through digital marketing experiments. These experiments provide invaluable insights into customer preferences, behaviors, and the overall effectiveness of marketing efforts, making them an essential component of any digital marketing strategy.

Digital marketing experiments matter for several reasons:

• Customer-centric approach: By conducting experiments, businesses can gain a deeper understanding of their target audience’s preferences and behaviors. This enables them to tailor their marketing efforts to better align with customer needs, resulting in more effective and engaging campaigns.
• Data-driven decision-making: Marketing experiments provide quantitative data on the performance of different marketing strategies and tactics. This empowers businesses to make informed decisions based on actual results rather than relying on intuition or guesswork. Ultimately, this data-driven approach leads to more efficient allocation of resources and improved marketing outcomes.
• Agility and adaptability: Businesses must be agile and adaptable to keep up with emerging trends and technologies. Digital marketing experiments allow businesses to test new ideas, platforms, and strategies in a controlled environment, helping them stay ahead of the curve and quickly respond to changing market conditions.
• Continuous improvement: Digital marketing experiments facilitate an iterative process of testing, learning, and refining marketing strategies. This ongoing cycle of improvement enables businesses to optimize their marketing efforts, drive better results, and maintain a competitive edge in the digital marketplace.
• ROI and profitability: By identifying which marketing tactics are most effective, businesses can allocate their marketing budget more efficiently and maximize their return on investment. This increased profitability can be reinvested into the business, fueling further growth and success.

Developing a culture of experimentation allows businesses to continuously improve their marketing strategies, maximize their ROI, and avoid being left behind by the competition.

The Fundamentals of Digital Marketing Experiments

Marketing experiments are structured tests that compare different marketing strategies, tactics, or assets to determine which one performs better in achieving specific objectives.

These experiments use a scientific approach, which involves formulating hypotheses, controlling variables, gathering data, and analyzing the results to make informed decisions.

Marketing experiments provide valuable insights into customer preferences and behaviors, enabling businesses to optimize their marketing efforts and maximize returns on investment (ROI).

There are several types of marketing experiments that businesses can use, depending on their objectives and available resources.

The most common types include:

A/B testing

A/B testing, also known as split testing, is a simple yet powerful technique that compares two variations of a single variable to determine which one performs better.

In an A/B test, the target audience is randomly divided into two groups: one group is exposed to version A (the control). In contrast, the other group is exposed to version B (the treatment). The performance of both versions is then measured and compared to identify the one that yields better results.

A/B testing can be applied to various marketing elements, such as headlines, calls-to-action, email subject lines, landing page designs, and ad copy. The primary advantage of A/B testing is its simplicity, making it easy for businesses to implement and analyze.

Multivariate testing

Multivariate testing is a more advanced technique that allows businesses to test multiple variables simultaneously.

In a multivariate test, several elements of a marketing asset are modified and combined to create different versions. These versions are then shown to different segments of the target audience, and their performance is measured and compared to determine the most effective combination of variables.

Multivariate testing is beneficial when optimizing complex marketing assets, such as websites or email templates, with multiple elements that may interact with one another. However, this method requires a larger sample size and more advanced analytical tools compared to A/B testing.

Pre-post analysis

Pre-post analysis involves comparing the performance of a marketing strategy before and after implementing a change.

This type of experiment is often used when it is not feasible to conduct an A/B or multivariate test, such as when the change affects the entire customer base or when there are external factors that cannot be controlled.

While pre-post analysis can provide useful insights, it is less reliable than A/B or multivariate testing because it does not account for potential confounding factors. To obtain accurate results from a pre-post analysis, businesses must carefully control for external influences and ensure that the observed changes are indeed due to the implemented modifications.

How To Start Growth Marketing Experiments

To conduct effective marketing experiments, businesses must pay attention to the following key elements:

Clear objectives

Having clear objectives is crucial for a successful marketing experiment. Before starting an experiment, businesses must identify the specific goals they want to achieve, such as increasing conversions, boosting engagement, or improving click-through rates. Clear objectives help guide the experimental design and ensure the results are relevant and actionable.

Hypothesis-driven approach

A marketing experiment should be based on a well-formulated hypothesis that predicts the expected outcome. A reasonable hypothesis is specific, testable, and grounded in existing knowledge or data. It serves as the foundation for experimental design and helps businesses focus on the most relevant variables and outcomes.

Proper experimental design

A marketing experiment requires a well-designed test that controls for potential confounding factors and ensures the reliability and validity of the results. This includes the random assignment of participants, controlling for external influences, and selecting appropriate variables to test. Proper experimental design increases the likelihood that observed differences are due to the tested variables and not other factors.

Adequate sample size

A successful marketing experiment requires an adequate sample size to ensure the results are statistically significant and generalizable to the broader target audience. The required sample size depends on the type of experiment, the expected effect size, and the desired level of confidence. In general, larger sample sizes provide more reliable and accurate results but may also require more resources to conduct the experiment.

Data-driven analysis

Marketing experiments rely on a data-driven analysis of the results. This involves using statistical techniques to determine whether the observed differences between the tested variations are significant and meaningful. Data-driven analysis helps businesses make informed decisions based on empirical evidence rather than intuition or gut feelings.

By understanding the fundamentals of marketing experiments and following best practices, businesses can gain valuable insights into customer preferences and behaviors, ultimately leading to improved outcomes and growth.

Setting up Your First Marketing Experiment

Embarking on your first marketing experiment can be both exciting and challenging. Following a systematic approach, you can set yourself up for success and gain valuable insights to improve your marketing efforts.

Here’s a step-by-step guide to help you set up your first marketing experiment.

Identifying your marketing objectives

Before diving into your experiment, it’s essential to establish clear marketing objectives. These objectives will guide your entire experiment, from hypothesis formulation to data analysis.

Consider what you want to achieve with your marketing efforts, such as increasing website conversions, improving open email rates, or boosting social media engagement.

Make sure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART) to ensure that they are actionable and provide meaningful insights.

Formulating a hypothesis

With your marketing objectives in mind, the next step is formulating a hypothesis for your experiment. A hypothesis is a testable prediction that outlines the expected outcome of your experiment. It should be based on existing knowledge, data, or observations and provide a clear direction for your experimental design.

For example, suppose your objective is to increase email open rates. In that case, your hypothesis might be, “Adding the recipient’s first name to the email subject line will increase the open rate by 10%.” This hypothesis is specific, testable, and clearly linked to your marketing objective.

Designing the experiment

Once you have a hypothesis in place, you can move on to designing your experiment. This involves several key decisions:

Choosing the right testing method:

Select the most appropriate testing method for your experiment based on your objectives, hypothesis, and available resources.

As discussed earlier, common testing methods include A/B, multivariate, and pre-post analyses. Choose the method that best aligns with your goals and allows you to effectively test your hypothesis.

Selecting the variables to test:

Identify the specific variables you will test in your experiment. These should be directly related to your hypothesis and marketing objectives. In the email open rate example, the variable to test would be the subject line, specifically the presence or absence of the recipient’s first name.

When selecting variables, consider their potential impact on your marketing objectives and prioritize those with the greatest potential for improvement. Also, ensure that the variables are easily measurable and can be manipulated in your experiment.

Identifying the target audience:

Determine the target audience for your experiment, considering factors such as demographics, interests, and behaviors. Your target audience should be representative of the larger population you aim to reach with your marketing efforts.

When segmenting your audience for the experiment, ensure that the groups are as similar as possible to minimize potential confounding factors.

In A/B or multivariate testing, this can be achieved through random assignment, which helps control for external influences and ensures a fair comparison between the tested variations.

Executing the experiment

With your experiment designed, it’s time to put it into action.

This involves several key considerations:

Timing and duration:

Choose the right timing and duration for your experiment based on factors such as the marketing channel, target audience, and the nature of the tested variables.

The duration of the experiment should be long enough to gather a sufficient amount of data for meaningful analysis but not so long that it negatively affects your marketing efforts or causes fatigue among your target audience.

In general, aim for a duration that allows you to reach a predetermined sample size or achieve statistical significance. This may vary depending on the specific experiment and the desired level of confidence.

Monitoring the experiment:

During the experiment, monitor its progress and performance regularly to ensure that everything is running smoothly and according to plan. This includes checking for technical issues, tracking key metrics, and watching for any unexpected patterns or trends.

If any issues arise during the experiment, address them promptly to prevent potential biases or inaccuracies in the results. Additionally, avoid making changes to the experimental design or variables during the experiment, as this can compromise the integrity of the results.

Analyzing the results

Once your experiment has concluded, it’s time to analyze the data and draw conclusions.

This involves two key aspects:

Statistical significance:

Statistical significance is a measure of the likelihood that the observed differences between the tested variations are due to the variables being tested rather than random chance. To determine statistical significance, you will need to perform a statistical test, such as a t-test or chi-squared test, depending on the nature of your data.

Generally, a result is considered statistically significant if the probability of the observed difference occurring by chance (the p-value) is less than a predetermined threshold, often set at 0.05 or 5%. This means there is a 95% confidence level that the observed difference is due to the tested variables and not random chance.

Practical significance:

While statistical significance is crucial, it’s also essential to consider the practical significance of your results. This refers to the real-world impact of the observed differences on your marketing objectives and business goals.

To assess practical significance, consider the effect size of the observed difference (e.g., the percentage increase in email open rates) and the potential return on investment (ROI) of implementing the winning variation. This will help you determine whether the experiment results are worth acting upon and inform your marketing decisions moving forward.

A systematic approach to designing growth marketing experiments helps you to design, execute, and analyze your experiment effectively, ultimately leading to better marketing outcomes and business growth.

Examples of Successful Marketing Experiments

In this section, we will explore three fictional case studies of successful marketing experiments that led to improved marketing outcomes. These examples will demonstrate the practical application of marketing experiments across different channels and provide valuable lessons that can be applied to your own marketing efforts.

Example 1: Redesigning a website for increased conversions

AcmeWidgets, an online store selling innovative widgets, noticed that its website conversion rate had plateaued.

They conducted a marketing experiment to test whether a redesigned landing page could improve conversions. They hypothesized that a more visually appealing and user-friendly design would increase conversion rates by 15%.

AcmeWidgets used A/B testing to compare their existing landing page (the control) with a new, redesigned version (the treatment). They randomly assigned website visitors to one of the two landing pages. They tracked conversions over a period of four weeks.

At the end of the experiment, AcmeWidgets found that the redesigned landing page had a conversion rate 18% higher than the control. The results were statistically significant, and the company decided to implement the new design across its entire website.

As a result, AcmeWidgets experienced a substantial increase in sales and revenue.

Example 2: Optimizing email marketing campaigns

EcoTravel, a sustainable travel agency, wanted to improve the open rates of their monthly newsletter. They hypothesized that adding a sense of urgency to the subject line would increase open rates by 10%.

To test this hypothesis, EcoTravel used A/B testing to compare two different subject lines for their newsletter:

• “Discover the world’s most beautiful eco-friendly destinations” (control)
• “Last chance to book: Explore the world’s most beautiful eco-friendly destinations” (treatment)

EcoTravel sent the newsletter to a random sample of their subscribers. Half received the control subject line, and the other half received the treatment. They then tracked the open rates for both groups over one week.

The results of the experiment showed that the treatment subject line, which included a sense of urgency, led to a 12% increase in open rates compared to the control.

Based on these findings, EcoTravel incorporated a sense of urgency in their future email subject lines to boost newsletter engagement.

Example 3: Improving social media ad performance

FitFuel, a meal delivery service for fitness enthusiasts, was looking to improve its Facebook ad campaign’s click-through rate (CTR). They hypothesized that using an image of a satisfied customer enjoying a FitFuel meal would increase CTR by 8% compared to their current ad featuring a meal image alone.

FitFuel conducted an A/B test on their Facebook ad campaign, comparing the performance of the control ad (meal image only) with the treatment ad (customer enjoying a meal). They targeted a similar audience with both ad variations and measured the CTR over two weeks. The experiment revealed that the treatment ad, featuring the customer enjoying a meal, led to a 10% increase in CTR compared to the control ad. FitFuel decided to update its

Facebook ad campaign with the new image, resulting in a more cost-effective campaign and higher return on investment.

Lessons learned from these examples

These fictional examples of successful marketing experiments highlight several key takeaways:

• Clearly defined objectives and hypotheses: In each example, the companies had specific marketing objectives and well-formulated hypotheses, which helped guide their experiments and ensure relevant and actionable results.
• Proper experimental design: Each company used the appropriate testing method for their experiment and carefully controlled variables, ensuring accurate and reliable results.
• Data-driven decision-making: The companies analyzed the data from their experiments to make informed decisions about implementing changes to their marketing strategies, ultimately leading to improved outcomes.
• Continuous improvement: These examples demonstrate that marketing experiments can improve marketing efforts continuously. By regularly conducting experiments and applying the lessons learned, businesses can optimize their marketing strategies and stay ahead of the competition.
• Relevance across channels: Marketing experiments can be applied across various marketing channels, such as website design, email campaigns, and social media advertising. Regardless of the channel, the principles of marketing experimentation remain the same, making them a valuable tool for marketers in diverse industries.

By learning from these fictional examples and applying the principles of marketing experimentation to your own marketing efforts, you can unlock valuable insights, optimize your marketing strategies, and achieve better results for your business.

Common Pitfalls of Marketing Experiments and How to Avoid Them

Conducting marketing experiments can be a powerful way to optimize your marketing strategies and drive better results.

However, it’s important to be aware of common pitfalls that can undermine the effectiveness of your experiments. In this section, we will discuss some of these pitfalls and provide tips on how to avoid them.

Insufficient sample size

An insufficient sample size can lead to unreliable results and limit the generalizability of your findings. When your sample size is too small, you run the risk of not detecting meaningful differences between the tested variations or incorrectly attributing the observed differences to random chance.

To avoid this pitfall, calculate the required sample size for your experiment based on factors such as the expected effect size, the desired level of confidence, and the type of statistical test you will use.

In general, larger sample sizes provide more reliable and accurate results but may require more resources to conduct the experiment. Consider adjusting your experimental design or testing methods to accommodate a larger sample size if necessary.

Lack of clear objectives

Your marketing experiment may not provide meaningful or actionable insights without clear objectives. Unclear objectives can lead to poorly designed experiments, irrelevant variables, or difficulty interpreting the results.

To prevent this issue, establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives before starting your experiment. These objectives should guide your entire experiment, from hypothesis formulation to data analysis, and ensure that your findings are relevant and useful for your marketing efforts.

Confirmation bias

Confirmation bias occurs when you interpret the results of your experiment in a way that supports your pre-existing beliefs or expectations. This can lead to inaccurate conclusions and suboptimal marketing decisions.

To minimize confirmation bias, approach your experiments with an open mind and be willing to accept results that challenge your assumptions.

Additionally, involve multiple team members in the data analysis process to ensure diverse perspectives and reduce the risk of individual biases influencing the interpretation of the results.

Overlooking external factors

External factors, such as changes in market conditions, seasonal fluctuations, or competitor actions, can influence the results of your marketing experiment and potentially confound your findings. Ignoring these factors may lead to inaccurate conclusions about the effectiveness of your marketing strategies.

To account for external factors, carefully control for potential confounding variables during the experimental design process. This might involve using random assignment, testing during stable periods, or controlling for known external influences.

Consider running follow-up experiments or analyzing historical data to confirm your findings and rule out the impact of external factors.

Tips for avoiding these pitfalls

By being aware of these common pitfalls and following best practices, you can ensure the success of your marketing experiments and obtain valuable insights for your marketing efforts. Here are some tips to help you avoid these pitfalls:

• Plan your experiment carefully: Invest time in the planning stage to establish clear objectives, calculate an adequate sample size, and design a robust experiment that controls for potential confounding factors.
• Use a hypothesis-driven approach: Formulate a specific, testable hypothesis based on existing knowledge or data to guide your experiment and focus on the most relevant variables and outcomes.
• Monitor your experiment closely: Regularly check the progress of your experiment, address any issues that arise, and ensure that your experiment is running smoothly and according to plan.
• Analyze your data objectively: Use statistical techniques to determine the significance of your results and consider the practical implications of your findings before making marketing decisions.
• Learn from your experiments: Apply the lessons learned from your experiments to continuously improve your marketing strategies and stay ahead of the competition.

By avoiding these common pitfalls and following best practices, you can increase the effectiveness of your marketing experiments, gain valuable insights into customer preferences and behaviors, and ultimately drive better results for your business.

Building a Culture of Experimentation

To truly reap the benefits of marketing experiments, it’s essential to build a culture of experimentation within your organization. This means fostering an environment where curiosity, learning, data-driven decision-making, and collaboration are valued and encouraged.

Encouraging curiosity and learning within your organization

Cultivating curiosity and learning starts with leadership. Encourage your team to ask questions, explore new ideas, and embrace a growth mindset.

Promote ongoing learning by providing resources, such as training programs, workshops, or access to industry events, that help your team stay up-to-date with the latest marketing trends and techniques.

Create a safe environment where employees feel comfortable sharing their ideas and taking calculated risks. Emphasize the importance of learning from both successes and failures and treat every experiment as an opportunity to grow and improve.

Adopting a data-driven mindset

A data-driven mindset is crucial for successful marketing experimentation. Encourage your team to make decisions based on data rather than relying on intuition or guesswork. This means analyzing the results of your experiments objectively, using statistical techniques to determine the significance of your findings, and considering the practical implications of your results before making marketing decisions.

To foster a data-driven culture, invest in the necessary tools and technologies to collect, analyze, and visualize data effectively. Train your team on how to use these tools and interpret the data to make informed marketing decisions.

Regularly review your data-driven efforts and adjust your strategies as needed to continuously improve and optimize your marketing efforts.

Integrating experimentation into your marketing strategy

Establish a systematic approach to conducting marketing experiments to fully integrate experimentation into your marketing strategy. This might involve setting up a dedicated team or working group responsible for planning, executing, and analyzing experiments or incorporating experimentation as a standard part of your marketing processes.

Create a roadmap for your marketing experiments that outlines each project’s objectives, hypotheses, and experimental designs. Monitor the progress of your experiments and adjust your roadmap as needed based on the results and lessons learned.

Ensure that your marketing team has the necessary resources, such as time, budget, and tools, to conduct experiments effectively. Set clear expectations for the role of experimentation in your marketing efforts and emphasize its importance in driving better results and continuous improvement.

Collaborating across teams for a holistic approach

Marketing experiments often involve multiple teams within an organization, such as design, product, sales, and customer support. Encourage cross-functional collaboration to ensure a holistic approach to experimentation and leverage each team’s unique insights and expertise.

Establish clear communication channels and processes for sharing information and results from your experiments. This might involve regular meetings, shared documentation, or internal presentations to keep all stakeholders informed and engaged.

Collaboration also extends beyond your organization. Connect with other marketing professionals, industry experts, and thought leaders to learn from their experiences, share your own insights, and stay informed about the latest trends and best practices in marketing experimentation.

By building a culture of experimentation within your organization, you can unlock valuable insights, optimize your marketing strategies, and drive better results for your business.

Encourage curiosity and learning, adopt a data-driven mindset, integrate experimentation into your marketing strategy, and collaborate across teams to create a strong foundation for marketing success.

If you’re new to marketing experiments, don’t be intimidated—start small and gradually expand your efforts as your confidence grows. By embracing a curious and data-driven mindset, even small-scale experiments can lead to meaningful insights and improvements.

As you gain experience, you can tackle more complex experiments and further refine your marketing strategies.

Remember, continuous learning and improvement is the key to success in marketing experimentation. By regularly conducting experiments, analyzing the results, and applying the lessons learned, you can stay ahead of the competition and drive better results for your business.

So, take the plunge and start experimenting today—your marketing efforts will be all the better.

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Expert Advice on Developing a Hypothesis for Marketing Experimentation 

• Conversion Rate Optimization

Simbar Dube

Every marketing experimentation process has to have a solid hypothesis. 

That’s a must – unless you want to be roaming in the dark and heading towards a dead-end in your experimentation program.

Hypothesizing is the second phase of our SHIP optimization process here at Invesp.

It comes after we have completed the research phase. 

This is an indication that we don’t just pull a hypothesis out of thin air. We always make sure that it is based on research data. 

But having a research-backed hypothesis doesn’t mean that the hypothesis will always be correct. In fact, tons of hypotheses bear inconclusive results or get disproved. 

The main idea of having a hypothesis in marketing experimentation is to help you gain insights – regardless of the testing outcome. 

By the time you finish reading this article, you’ll know: 

• The essential tips on what to do when crafting a hypothesis for marketing experiments
• How a marketing experiment hypothesis works 

How experts develop a solid hypothesis

The basics: marketing experimentation hypothesis.

A hypothesis is a research-based statement that aims to explain an observed trend and create a solution that will improve the result. This statement is an educated, testable prediction about what will happen.

It has to be stated in declarative form and not as a question.

“ If we add magnification info, product video and making virtual mirror buttons, will that improve engagement? ” is not declarative, but “ Improving the experience of product pages by adding magnification info, product video and making virtual mirror buttons will increase engagement ” is.

Here’s a quick example of how a hypothesis should be phrased: 

• Replacing ___ with __ will increase [conversion goal] by [%], because:
• Removing ___ and __ will decrease [conversion goal] by [%], because:
• Changing ___ into __ will not affect [conversion goal], because:
• Improving  ___ by  ___will increase [conversion goal], because: 

As you can see from the above sentences, a good hypothesis is written in clear and simple language. Reading your hypothesis should tell your team members exactly what you thought was going to happen in an experiment.

Another important element of a good hypothesis is that it defines the variables in easy-to-measure terms, like who the participants are, what changes during the testing, and what the effect of the changes will be: 

Example : Let’s say this is our hypothesis: 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Who are the participants : 

Visitors. 

What changes during the testing : 

Displaying full look items on every “continue shopping & view your bag” pop-up and highlighting the value of having a full look…

What the effect of the changes will be:  

Will improve the visibility of a full look, encourage visitors to add multiple items from the same look and that will increase the average order value, quantity with cross-selling by 3% .

Don’t bite off more than you can chew! Answering some scientific questions can involve more than one experiment, each with its own hypothesis. so, you have to make sure your hypothesis is a specific statement relating to a single experiment.

How a Marketing Experimentation Hypothesis Works

Assuming that you have done conversion research and you have identified a list of issues ( UX or conversion-related problems) and potential revenue opportunities on the site. The next thing you’d want to do is to prioritize the issues and determine which issues will most impact the bottom line.

Having ranked the issues you need to test them to determine which solution works best. At this point, you don’t have a clear solution for the problems identified. So, to get better results and avoid wasting traffic on poor test designs, you need to make sure that your testing plan is guided. 

This is where a hypothesis comes into play. 

For each and every problem you’re aiming to address, you need to craft a hypothesis for it – unless the problem is a technical issue that can be solved right away without the need to hypothesize or test. 

One important thing you should note about an experimentation hypothesis is that it can be implemented in different ways.  

This means that one hypothesis can have four or five different tests as illustrated in the image above. Khalid Saleh , the Invesp CEO, explains: 

“There are several ways that can be used to support one single hypothesis. Each and every way is a possible test scenario. And that means you also have to prioritize the test design you want to start with. Ultimately the name of the game is you want to find the idea that has the biggest possible impact on the bottom line with the least amount of effort. We use almost 18 different metrics to score all of those.”

In one of the recent tests we launched after watching video recordings, viewing heatmaps, and conducting expert reviews, we noticed that:  

• Visitors were scrolling to the bottom of the page to fill out a calculator so as to get a free diet plan. 
• Brand is missing 
• Too many free diet plans – and this made it hard for visitors to choose and understand.  
• No value proposition on the page
• The copy didn’t mention the benefits of the paid program
• There was no clear CTA for the next action

To help you understand, let’s have a look at how the original page looked like before we worked on it: 

So our aim was to make the shopping experience seamless for visitors, make the page more appealing and not confusing. In order to do that, here is how we phrased the hypothesis for the page above: 

Improving the experience of optin landing pages by making the free offer accessible above the fold and highlighting the next action with a clear CTA and will increase the engagement on the offer and increase the conversion rate by 1%.

For this particular hypothesis, we had two design variations aligned to it:

The two above designs are different, but they are aligned to one hypothesis. This goes on to show how one hypothesis can be implemented in different ways. Looking at the two variations above – which one do you think won?

Yes, you’re right, V2 was the winner. 

Considering that there are many ways you can implement one hypothesis, so when you launch a test and it fails, it doesn’t necessarily mean that the hypothesis was wrong. Khalid adds:

“A single failure of a test doesn’t mean that the hypothesis is incorrect. Nine times out of ten it’s because of the way you’ve implemented the hypothesis. Look at the way you’ve coded and look at the copy you’ve used – you are more likely going to find something wrong with it. Always be open.” 

So there are three things you should keep in mind when it comes to marketing experimentation hypotheses: 

• It takes a while for this hypothesis to really fully test it.
• A single failure doesn’t necessarily mean that the hypothesis is incorrect.
• Whether a hypothesis is proved or disproved, you can still learn something about your users.

I know it’s never easy to develop a hypothesis that informs future testing – I mean it takes a lot of intense research behind the scenes, and tons of ideas to begin with. So, I reached out to six CRO experts for tips and advice to help you understand more about developing a solid hypothesis and what to include in it. 

Maurice   says that a solid hypothesis should have not more than one goal: 

Maurice Beerthuyzen – CRO/CXO Lead at ClickValue “Creating a hypothesis doesn’t begin at the hypothesis itself. It starts with research. What do you notice in your data, customer surveys, and other sources? Do you understand what happens on your website? When you notice an opportunity it is tempting to base one single A/B test on one hypothesis. Create hypothesis A and run a single test, and then move forward to the next test. With another hypothesis. But it is very rare that you solve your problem with only one hypothesis. Often a test provides several other questions. Questions which you can solve with running other tests. But based on that same hypothesis! We should not come up with a new hypothesis for every test. Another mistake that often happens is that we fill the hypothesis with multiple goals. Then we expect that the hypothesis will work on conversion rate, average order value, and/or Click Through Ratio. Of course, this is possible, but when you run your test, your hypothesis can only have one goal at once. And what if you have two goals? Just split the hypothesis then create a secondary hypothesis for your second goal. Every test has one primary goal. What if you find a winner on your secondary hypothesis? Rerun the test with the second hypothesis as the primary one.”

Jon believes that a strong hypothesis is built upon three pillars:

Jon MacDonald – President and Founder of The Good Respond to an established challenge – The challenge must have a strong background based on data, and the background should state an established challenge that the test is looking to address. Example: “Sign up form lacks proof of value, incorrectly assuming if users are on the page, they already want the product.” Propose a specific solution – What is the one, the single thing that is believed will address the stated challenge? Example: “Adding an image of the dashboard as a background to the signup form…”. State the assumed impact – The assumed impact should reference one specific, measurable optimization goal that was established prior to forming a hypothesis. Example: “…will increase signups.” So, if your hypothesis doesn’t have a specific, measurable goal like “will increase signups,” you’re not really stating a test hypothesis!”

Matt uses his own hypothesis builder to collate important data points into a single hypothesis. 

Matt Beischel – Founder of Corvus CRO Like Jon, Matt also breaks down his hypothesis writing process into three sections. Unlike Jon, Matt sections are: Comprehension Response Outcome I set it up so that the names neatly match the “CRO.” It’s a sort of “mad-libs” style fill-in-the-blank where each input is an important piece of information for building out a robust hypothesis. I consider these the minimum required data points for a good hypothesis; if you can’t completely fill out the form, then you don’t have a good hypothesis. Here’s a breakdown of each data point: Comprehension – Identifying something that can be improved upon Problem: “What is a problem we have?” Observation Method: “How did we identify the problem?” Response – Change that can cause improvement Variation: “What change do we think could solve the problem?” Location: “Where should the change occur?” Scope: “What are the conditions for the change?” Audience: “Who should the change affect?” Outcome – Measurable result of the change that determines the success Behavior Change : “What change in behavior are we trying to affect?” Primary KPI: “What is the important metric that determines business impact?” Secondary KPIs: “Other metrics that will help reinforce/refute the Primary KPI” Something else to consider is that I have a “user first” approach to formulating hypotheses. My process above is always considered within the context of how it would first benefit the user. Now, I do feel that a successful experiment should satisfy the needs of BOTH users and businesses, but always be in favor of the user. Notice that “Behavior Change” is the first thing listed in Outcome, not primary business KPI. Sure, at the end of the day you are working for the business’s best interests (both strategically and financially), but placing the user first will better inform your decision making and prioritization; there’s a reason that things like personas, user stories, surveys, session replays, reviews, etc. exist after all. A business-first ideology is how you end up with dark patterns and damaging brand credibility.”

One of the many mistakes that CROs make when writing a hypothesis is that they are focused on wins and not on insights. Shiva advises against this mindset:

Shiva Manjunath – Marketing Manager and CRO at Gartner “Test to learn, not test to win. It’s a very simple reframe of hypotheses but can have a magnitude of difference. Here’s an example: Test to Win Hypothesis: If I put a product video in the middle of the product page, I will improve add to cart rates and improve CVR. Test to Learn Hypothesis: If I put a product video on the product page, there will be high engagement with the video and it will positively influence traffic What you’re doing is framing your hypothesis, and test, in a particular way to learn as much as you can. That is where you gain marketing insights. The more you run ‘marketing insight’ tests, the more you will win. Why? The more you compound marketing insight learnings, your win velocity will start to increase as a proxy of the learnings you’ve achieved. Then, you’ll have a higher chance of winning in your tests – and the more you’ll be able to drive business results.”

Lorenzo  says it’s okay to focus on achieving a certain result as long as you are also getting an answer to: “Why is this event happening or not happening?”

Lorenzo Carreri – CRO Consultant “When I come up with a hypothesis for a new or iterative experiment, I always try to find an answer to a question. It could be something related to a problem people have or an opportunity to achieve a result or a way to learn something. The main question I want to answer is “Why is this event happening or not happening?” The question is driven by data, both qualitative and quantitative. The structure I use for stating my hypothesis is: From [data source], I noticed [this problem/opportunity] among [this audience of users] on [this page or multiple pages]. So I believe that by [offering this experiment solution], [this KPI] will [increase/decrease/stay the same].

Jakub Linowski says that hypotheses are meant to hold researchers accountable:

Jakub Linowski – Chief Editor of GoodUI “They do this by making your change and prediction more explicit. A typical hypothesis may be expressed as: If we change (X), then it will have some measurable effect (A). Unfortunately, this oversimplified format can also become a heavy burden to your experiment design with its extreme reductionism. However you decide to format your hypotheses, here are three suggestions for more flexibility to avoid limiting yourself. One Or More Changes To break out of the first limitation, we have to admit that our experiments may contain a single or multiple changes. Whereas the classic hypothesis encourages a single change or isolated variable, it’s not the only way we can run experiments. In the real world, it’s quite normal to see multiple design changes inside a single variation. One valid reason for doing this is when wishing to optimize a section of a website while aiming for a greater effect. As more positive changes compound together, there are times when teams decide to run bigger experiments. An experiment design (along with your hypotheses) therefore should allow for both single or multiple changes. One Or More Metrics A second limitation of many hypotheses is that they often ask us to only make a single prediction at a time. There are times when we might like to make multiple guesses or predictions to a set of metrics. A simple example of this might be a trade-off experiment with a guess of increased sales but decreased trial signups. Being able to express single or multiple metrics in our experimental designs should therefore be possible. Estimates, Directional Predictions, Or Unknowns Finally, traditional hypotheses also tend to force very simple directional predictions by asking us to guess whether something will increase or decrease. In reality, however, the fidelity of predictions can be higher or lower. On one hand, I’ve seen and made experiment estimations that contain specific numbers from prior data (ex: increase sales by 14%). While at other times it should also be acceptable to admit the unknown and leave the prediction blank. One example of this is when we are testing a completely novel idea without any prior data in a highly exploratory type of experiment. In such cases, it might be dishonest to make any sort of predictions and we should allow ourselves to express the unknown comfortably.”

Conclusion 

So there you have it! Before you jump on launching a test, start by making sure that your hypothesis is solid and backed by research. Ask yourself the questions below when crafting a hypothesis for marketing experimentation:

• Is the hypothesis backed by research?
• Can the hypothesis be tested?
• Does the hypothesis provide insights?
• Does the hypothesis set the expectation that there will be an explanation behind the results of whatever you’re testing?

Don’t worry! Hypothesizing may seem like a very complicated process, but it’s not complicated in practice especially when you have done proper research.

If you enjoyed reading this article and you’d love to get the best CRO content – delivered by the best experts in the industry – straight to your inbox, every week. Please subscribe here .

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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

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A/B Testing in Digital Marketing: Example of four-step hypothesis framework

by Daniel Burstein , Senior Director, Content & Marketing, MarketingSherpa and MECLABS Institute

This article was originally published in the MarketingSherpa email newsletter .

If you are a marketing expert — whether in a brand’s marketing department or at an advertising agency — you may feel the need to be absolutely sure in an unsure world.

What should the headline be? What images should we use? Is this strategy correct? Will customers value this promo?

This is the stuff you’re paid to know. So you may feel like you must boldly proclaim your confident opinion.

But you can’t predict the future with 100% accuracy. You can’t know with absolute certainty how humans will behave. And let’s face it, even as marketing experts we’re occasionally wrong.

It’s not bad, it’s healthy. And the most effective way to overcome that doubt is by testing our marketing creative to see what really works.

Developing a hypothesis

After we published Value Sequencing: A step-by-step examination of a landing page that generated 638% more conversions , a MarketingSherpa reader emailed us and asked …

Great stuff Daniel. Much appreciated. I can see you addressing all the issues there.

I thought I saw one more opportunity to expand on what you made. Would you consider adding the IF, BY, WILL, BECAUSE to the control/treatment sections so we can see what psychology you were addressing so we know how to create the hypothesis to learn from what the customer is currently doing and why and then form a test to address that? The video today on customer theory was great (Editor’s Note: Part of the MarketingExperiments YouTube Live series ) . I think there is a way to incorporate that customer theory thinking into this article to take it even further.

Developing a hypothesis is an essential part of marketing experimentation. Qualitative-based research should inform hypotheses that you test with real-world behavior.

The hypotheses help you discover how accurate those insights from qualitative research are. If you engage in hypothesis-driven testing, then you ensure your tests are strategic (not just based on a random idea) and built in a way that enables you to learn more and more about the customer with each test.

And that methodology will ultimately lead to greater and greater lifts over time, instead of a scattershot approach where sometimes you get a lift and sometimes you don’t, but you never really know why.

Here is a handy tool to help you in developing hypotheses — the MECLABS Four-Step Hypothesis Framework.

As the reader suggests, I will use the landing page test referenced in the previous article as an example. ( Please note: While the experiment in that article was created with a hypothesis-driven approach, this specific four-step framework is fairly new and was not in common use by the MECLABS team at that time, so I have created this specific example after the test was developed based on what I see in the test).

Here is what the hypothesis would look like for that test, and then we’ll break down each part individually:

If we emphasize the process-level value by adding headlines, images and body copy, we will generate more leads because the value of a longer landing page in reducing the anxiety of calling a TeleAgent outweighs the additional friction of a longer page.

IF: Summary description

The hypothesis begins with an overall statement about what you are trying to do in the experiment. In this case, the experiment is trying to emphasize the process-level value proposition (one of the four essential levels of value proposition ) of having a phone call with a TeleAgent.

The control landing page was emphasizing the primary value proposition of the brand itself.

The treatment landing page is essentially trying to answer this value proposition question: If I am your ideal customer, why should I call a TeleAgent rather than take any other action to learn more about my Medicare options?

The control landing page was asking a much bigger question that customers weren’t ready to say “yes” to yet, and it was overlooking the anxiety inherent in getting on a phone call with someone who might try to sell you something: If I am your ideal customer, why should I buy from your company instead of any other company.

This step answers WHAT you are trying to do.

BY: Remove, add, change

The next step answers HOW you are going to do it.

As Flint McGlaughlin, CEO and Managing Director of MECLABS Institute teaches, there are only three ways to improve performance: removing, adding or changing .

In this case, the team focused mostly on adding — adding headlines, images and body copy that highlighted the TeleAgents as trusted advisors.

“Adding” can be counterintuitive for many marketers. The team’s original landing page was short. Conventional wisdom says customers won’t read long landing pages. When I’m presenting to a group of marketers, I’ll put a short and long landing page on a slide and ask which page they think achieved better results.

Invariably I will hear, “Oh, the shorter page. I would never read something that long.”

That first-person statement is a mistake. Your marketing creative should not be based on “I” — the marketer. It should be based on “they” — the customer.

Most importantly, you need to focus on the customer at a specific point in time — when he or she is in the mindspace of considering to take an action like purchase a product or in need of more information before they decide to download a whitepaper. And sometimes in these situations, longer landing pages perform better.

In the case of this landing page, even the customer may not necessarily favor a long landing page all the time. But in the real-world situation when they are considering whether to call a TeleAgent or not, the added value helps more customers decide to take the action.

WILL: Improve performance

This is your KPI (key performance indicator). This step answers another HOW question: How do you know your hypothesis has been supported or refuted?

You can choose secondary metrics to monitor during your test as well. This might help you interpret the customer behavior observed in the test.

But ultimately, the hypothesis should rest on a single metric.

For this test, the goal was to generate more leads. And the treatment did — 638% more leads.

BECAUSE: Customer insight

This last step answers a WHY question — why did the customers act this way?

This helps you determine what you can learn about customers based on the actions observed in the experiment.

This is ultimately why you test. To learn about the customer and continually refine your company’s customer theory .

In this case, the team theorized that the value of a longer landing page in reducing the anxiety of calling a TeleAgent outweighs the additional friction of a longer landing page.

And the test results support that hypothesis.

Related Resources

The Hypothesis and the Modern-Day Marketer

Boost your Conversion Rate with a MECLABS Quick Win Intensive

Designing Hypotheses that Win: A four-step framework for gaining customer wisdom and generating marketing results

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A/B Testing: Example of a good hypothesis

Want to know the secret to always running successful tests?

The answer is to formulate a hypothesis .

Now when I say it’s always successful, I’m not talking about always increasing your Key Performance Indicator (KPI). You can “lose” a test, but still be successful.

That sounds like an oxymoron, but it’s not. If you set up your test strategically, even if the test decreases your KPI, you gain a learning , which is a success! And, if you win, you simultaneously achieve a lift and a learning. Double win!

The way you ensure you have a strategic test that will produce a learning is by centering it around a strong hypothesis.

So, what is a hypothesis?

By definition, a hypothesis is a proposed statement made on the basis of limited evidence that can be proved or disproved and is used as a starting point for further investigation.

Let’s break that down:

It is a proposed statement.

• A hypothesis is not fact, and should not be argued as right or wrong until it is tested and proven one way or the other.

It is made on the basis of limited (but hopefully some ) evidence.

• Your hypothesis should be informed by as much knowledge as you have. This should include data that you have gathered, any research you have done, and the analysis of the current problems you have performed.

It can be proved or disproved.

• A hypothesis pretty much says, “I think by making this change , it will cause this effect .” So, based on your results, you should be able to say “this is true” or “this is false.”

It is used as a starting point for further investigation.

• The key word here is starting point . Your hypothesis should be formed and agreed upon before you make any wireframes or designs as it is what guides the design of your test. It helps you focus on what elements to change, how to change them, and which to leave alone.

How do I write a hypothesis?

The structure of your basic hypothesis follows a CHANGE: EFFECT framework.

While this is a truly scientific and testable template, it is very open-ended. Even though this hypothesis, “Changing an English headline into a Spanish headline will increase clickthrough rate,” is perfectly valid and testable, if your visitors are English-speaking, it probably doesn’t make much sense.

So now the question is …

How do I write a GOOD hypothesis?

To quote my boss Tony Doty , “This isn’t Mad Libs.”

We can’t just start plugging in nouns and verbs and conclude that we have a good hypothesis. Your hypothesis needs to be backed by a strategy. And, your strategy needs to be rooted in a solution to a problem .

So, a more complete version of the above template would be something like this:

In order to have a good hypothesis, you don’t necessarily have to follow this exact sentence structure, as long as it is centered around three main things:

Presumed problem

Proposed solution

Anticipated result

After you’ve completed your analysis and research, identify the problem that you will address. While we need to be very clear about what we think the problem is, you should leave it out of the hypothesis since it is harder to prove or disprove. You may want to come up with both a problem statement and a hypothesis .

For example:

Problem Statement: “The lead generation form is too long, causing unnecessary friction .”

Hypothesis: “By changing the amount of form fields from 20 to 10, we will increase number of leads.”

When you are thinking about the solution you want to implement, you need to think about the psychology of the customer. What psychological impact is your proposed problem causing in the mind of the customer?

For example, if your proposed problem is “There is a lack of clarity in the sign-up process,” the psychological impact may be that the user is confused.

Now think about what solution is going to address the problem in the customer’s mind. If they are confused, we need to explain something better, or provide them with more information. For this example, we will say our proposed solution is to “Add a progress bar to the sign-up process.”  This leads straight into the anticipated result.

If we reduce the confusion in the visitor’s mind (psychological impact) by adding the progress bar, what do we foresee to be the result? We are anticipating that it would be more people completing the sign-up process. Your proposed solution and your KPI need to be directly correlated.

Note: Some people will include the psychological impact in their hypothesis. This isn’t necessarily wrong, but we do have to be careful with assumptions. If we say that the effect will be “Reduced confusion and therefore increase in conversion rate,” we are assuming the reduced confusion is what made the impact. While this may be correct, it is not measureable and it is hard to prove or disprove.

To summarize, your hypothesis should follow a structure of: “If I change this, it will have this effect,” but should always be informed by an analysis of the problems and rooted in the solution you deemed appropriate.

Related Resources:

A/B Testing 101: How to get real results from optimization

The True Value of Data

15 Years of Marketing Research in 11 Minutes

Marketing Analytics: 6 simple steps for interpreting your data

Website A/B Testing: 4 tips to beat an unbeatable landing page

Online Cart: 6 ideas to test and optimize your checkout process

B2B Gamification: Autodesk’s two approaches to in-trial marketing [Video]

How to Discover Exactly What the Customer Wants to See on the Next Click: 3 critical…

The 21 Psychological Elements that Power Effective Web Design (Part 3)

The 21 Psychological Elements that Power Effective Web Design (Part 2)

The 21 Psychological Elements that Power Effective Web Design (Part 1)

Thanks for the article. I’ve been trying to wrap my head around this type of testing because I’d like to use it to see the effectiveness on some ads. This article really helped. Thanks Again!

Hey Lauren, I am just getting to the point that I have something to perform A-B testing on. This post led me to this site which will and already has become a help in what to test and how to test .

Again, thanks for getting me here .

Good article. I have been researching different approaches to writing testing hypotheses and this has been a help. The only thing I would add is that it can be useful to capture the insight/justification within the hypothesis statement. IF i do this, THEN I expect this result BECAUSE I have this insight.

@Kaya Great!

Good article – but technically you can never prove an hypothesis, according to the principle of falsification (Popper), only fail to disprove the null hypothesis.

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How to use a Hypothesis for Marketing Analytics

• September 5, 2023
• Digital Marketing , Social Media

Hypothesis is a much-underused concept in marketing analytics that can yield significant results. It is a method of testing a marketing theory or proposition before investing substantial resources into implementation. Using this approach can ensure that resources are prioritized to achieve better outcomes. So, let’s dive into the hypothesis and how it can be used in marketing analytics.

What is a Hypothesis?

A hypothesis is a statement derived from a prospective marketing action or change to the existing strategy that might potentially ameliorate a current predicament.

For instance, your website’s CTA button’s lack of action may inflict a high bounce rate, which can draw up a hypothesis. “Adjusting the CTA button’s position and color will reduce the bounce rate.

“This proposition is the hypothesis, and the objective of the analysis is to either back it up with trustworthy data or conclude that it isn’t helpful or practical.

Why Use Hypothesis in Marketing Analytics?

The answer is simple: using a hypothesis can improve analytical accuracy, detect unexpected outcomes, and reduce unnecessary costs.

By conducting a hypothesis before implementing marketing campaigns , companies can save money and time by filtering out unpromising alternatives.

Understanding the Hypothesis for Marketing Analytics

Marketing has become one of the most important aspects of modern-day business operations. With the ever-increasing competition in the market, businesses need to leverage various technological tools and analytical techniques to understand consumer behavior and make informed decisions about their marketing strategies .

One such tool that has gained immense popularity in recent years is marketing analytics. Marketing analytics helps businesses gather, analyze, and interpret large data sets to draw meaningful insights about their target audience.

We will discuss the hypothesis for marketing analytics and how it can help businesses make data-driven decisions.

Understanding the hypothesis is crucial for any marketing analytics project. An idea is based on previous data, research, and assumptions about a problem or opportunity.

In other words, it is an assumption the analyst makes about the relationship between variables.

The hypothesis for marketing analytics should be created based on the analysis objectives and the available data. It can help businesses identify patterns, trends, and relationships between variables that can inform their marketing strategies.

Hypothesis-Driven Analysis Method

This analytical method follows a plan of action that starts with listing the presumptive reasons for the problem. In continuation, we write out the solution or change required, and it ends by stating the critical overviews and measurable factors held up by the assumption.

The next stage is to execute a hypothesis and determine whether a desired outcome is achievable. It can be done by testing the proposition on a subset of the audience, A/B testing , depending on available resources.

Key Hypothesis Components

A well-constructed hypothesis contains two vital components – the problem statement and the proposed solution.

The problem statement in hypothesis detection starts by examining an existing issue’s hypotheses to find a solution, e.g., low conversion rates on the landing page. When proposing a solution, retain prospects that are reachable and measurable.

A hypothesis’s remedy should also include a specific assumption, for example, “Changing the font’s color and size may increase transparency on the landing page and drive up conversion rates.”

How to Use a Hypothesis for Marketing Analytics: A Step-by-Step Guide

Are you tired of making major marketing decisions based solely on gut feelings? Do you wish there was a way to make data-driven decisions with confidence?

Look no further than hypothesis testing! Hypothesis testing is a statistical method that allows you to validate or reject assumptions about your data.

In marketing analytics, this method can help you identify the most effective strategies and make informed decisions about where to invest your resources. Below, we’ll explore the steps for using a hypothesis in your marketing analytics.

Decoding the Hypothesis for Marketing Analytics

As a marketer, you aim to build an effective strategy to reach your audience and increase conversions. But how do you identify the factors that contribute to your success?

Marketing analytics is the solution that helps you create data-backed decisions. However, without a hypothesis, analytics is just a bunch of numbers. It aims to help you understand how to define a marketing hypothesis, its components, and its advantages.

Firstly, let’s define a marketing hypothesis. It is a statement that predicts how a particular marketing strategy, tactic, or change in the marketing mix will affect your desired outcome. The idea usually starts with an ‘if-then’ statement.

For example, “If we increase our social media advertising budget, then we will increase website traffic by 20%.” This statement predicts that increasing social media advertising will cause website traffic to increase.

Identify Your Hypothesis

Before you start your analysis, you need to state what you think will happen. This is your hypothesis. Your hypothesis should be specific and measurable.

For example, if you want to test whether adding a phone number to your website leads to more leads, your hypothesis might be, “Adding a phone number to the website will increase lead generation by 20%.”

Choose a Statistical Test

Once you have your hypothesis, it’s time to choose a statistical test to validate it. The type of test you choose will depend on the kind of data you have and what you’re trying to prove.

For example, you might use a t-test comparing two groups. If you’re comparing multiple groups, you might use an ANOVA. You might use a Pearson’s r test if you’re looking for a correlation.

Many resources are available online to help you choose the proper test for your hypothesis.

Collect Your Data

Before you can run your statistical test, you need to collect data. Ensure you collect enough data to ensure your results are statistically significant.

You can use tools like Google Analytics, HubSpot, or Salesforce to collect data on website traffic, leads generated, and other vital metrics.

Run Your Test

Now, it’s time to put your hypothesis and data to the test! Run your chosen statistical test and compare the results to your idea. If the results match your vision, congratulations!

You’ve validated your assumption. If the results don’t match your hypothesis, don’t panic. Use the data to refine your idea and try again.

Draw Conclusions and Make Decisions

Once you’ve validated your hypothesis, it’s time to draw conclusions and make decisions. Use your data to determine whether your marketing efforts are working or changes are needed. This data-driven approach will help you make informed decisions and increase your chances of success.

Conclusion:

The marketing world is uncertain, and every marketer must make decisions based on approximations that may or may not bring success.

Using hypotheses in marketing analytics provides a reliable and cheaper way of testing speculation and determining more efficiently what works and what doesn’t. Particularly in handling intense competition and limited resources, marketing teams must maximize their investments.

Hypothesis-driven analysis ensures that marketing choices are driven by data and logic, reducing the probability of making poor judgments. So, start engaging with hypotheses and make informed decisions that benefit users and the business growth rate.

Call: +91 9848321284

Email:  [email protected]

Kiran Voleti

Kiran Voleti is an Entrepreneur , Digital Marketing Consultant , Social Media Strategist , Internet Marketing Consultant, Creative Designer and Growth Hacker.

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Alzheimer’s Disease: An Overview of Major Hypotheses and Therapeutic Options in Nanotechnology

Mugdha agarwal.

1 Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, India; moc.liamg@1ahdgumlawraga

Mohammad Rizwan Alam

2 Department of Medical Genetics, School of Medicine, Keimyung University, Daegu 42601, Korea; moc.liamg@1002nawzirdm

Mohd Kabir Haider

3 Vellore Institute of Technology, Vellore 600127, India; moc.liamg@0002rediahribak

Md. Zubbair Malik

4 School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India

Dae-Kwang Kim

5 Hanvit Institute for Medical Genetics, Daegu 42601, Korea

Associated Data

All data have been illustrated in the manuscript.

Alzheimer’s disease (AD), a progressively fatal neurodegenerative disorder, is the most prominent form of dementia found today. Patients suffering from Alzheimer’s begin to show the signs and symptoms, like decline in memory and cognition, long after the cellular damage has been initiated in their brain. There are several hypothesis for the neurodegeneration process; however, the lack of availability of in vivo models makes the recapitulation of AD in humans impossible. Moreover, the drugs currently available in the market serve to alleviate the symptoms and there is no cure for the disease. There have been two major hurdles in the process of finding the same—the inefficiency in cracking the complexity of the disease pathogenesis and the inefficiency in delivery of drugs targeted for AD. This review discusses the different drugs that have been designed over the recent years and the drug delivery options in the field of nanotechnology that have been found most feasible in surpassing the blood–brain barrier (BBB) and reaching the brain.

1. Introduction

The most prevalent cause of dementia is Alzheimer’s disease (AD), a condition that affects approximately 50 million people worldwide, and the case of dementia is estimated to reach 131.5 million by the year 2050 [ 1 ]. AD is characterized by cognitive decline, behavioral change and inability to perform daily life activity [ 2 , 3 ]. Lack of successful Aβ clearance are thought to cause the onset or development of AD in most situations [ 4 , 5 , 6 , 7 ]. Available drugs that lower Aβ has been ineffective in preventing cognitive decline [ 8 , 9 , 10 ]. Despite continuous efforts by researchers towards finding a cure for the disease, more than a century since AD was first discovered, we have still been unable to come up with any significant treatment option, owing mainly to the lack of efficient drug delivery methods and several loopholes in the conventional drugs focusing on the symptomatic management of the disease and these drugs unlikely to stop the disease development [ 11 , 12 , 13 ].

Currently the FDA-approved drugs for AD in the market have limitations like high dosage regimes, low bioavailability, gastrointestinal tract side effects and ineffectual brain targeting, which ultimately lead to incompliance with the patient and discontinuation of the treatment [ 14 , 15 ]. This is where the role of nanotechnology comes into play. Advancements in this field have given rise to ease in the delivery of therapeutic molecules across the BBB and reaching the central nervous system (CNS) [ 16 ], along with the removal of other aforementioned impediments in the treatment process of AD.

2. Pathophysiology of the Disease

Alzheimer’s is characterized by the presence of amyloid beta plaques and neurofibrillary tangles that are formed in the patient’s brain [ 17 ]. Since the disease’s pathogenesis is multifactorial, the detection of behavioral and memory changes is difficult [ 18 , 19 ]. The mutations in three major genes encoding—amyloid precursor protein (APP) on chromosome 21, Presenilin-1 (PS1) on chromosome 14 and Presenilin-2 (PS2) are reported to be responsible for the formation of the same [ 20 ]. The mutations in these genes lead amyloid-β protein (Aβ) to form senile plaques in the extracellular region and the hyper phosphorylation of Tau protein that forms the neurofibrillary tangles intracellularly [ 21 ]. This causes widespread damage to nerve cells throughout the brain cortex, accompanied by early loss of cholinergic neurons from the basal region of the forebrain. There are a number of hypotheses that aid in the therapeutic formulation for AD and that have been discussed before. Some pharmacological treatments available for Alzheimer’s disease are shown in Table 1 .

Pharmacological treatments available for Alzheimer’s disease.

ER: Extended release, MOA: Mechanism of action, AChE: Acetylcholine esterase, NMDA: N-methyl-D-aspartate. Source: [NIH Publication, 2008, https://www.uspharmacist.com/article/alzheimers-disease-increasing-numbers-but-no-cure ].

2.1. The Amyloid-Beta Hypothesis

This hypothesis is the most recognized one amongst researchers, owing to its explanation for the senile plaque formation and the accumulation of Aβ oligomers as the major highlight of the disease [ 22 ]. The proteolysis of transmembrane protein APP by beta and gamma secretases forms single units of Aβ, which further undergo certain structural modifications to form sheets of oligomers that are harmful in nature. These oligomeric sheets aggregate to form plaques and tangles. The Aβ protein has two subunits—Aβ40 and Aβ42, where the latter is soluble. The APP is normally cleared by an enzyme called alpha secretase, which yields sAPP-alpha [ 23 , 24 , 25 ]. The sAPP-alpha is responsible for memory and learning activities of the brain, fighting against stress conditions and in maintaining neuronal excitability. In the diseased condition, the APP is cleaved by beta secretase into sAPP-beta and C99 fraction, which is membrane bound. Gamma secretase acts upon the C99 fraction producing either Aβ40 or Aβ42, which cause the plaques to deposit [ 26 , 27 , 28 ]. This disrupts the normal functioning of sAPP, leading to metabolic changes, decreased neuronal excitability, conditions favoring oxidative stress and dysregulated calcium homeostasis.

Recently, it has been discovered that APP cleavage occurs by a third way involving η-secretase [ 29 ]. The η-secretase is found to cleave APP at amino acids 504–505, which generates carboxy-terminal fragments Aη-α and Aη-β of higher molecular mass after undergoing a second cleavage by α-and β-secretase, respectively. An Aβ (1–16) fragment is contained by the Aη-α sequence, which is found to be neurotoxic. Aβ plays a role in memory and synaptic plasticity, although its proper function in the brain remains unknown yet [ 30 ]. AD has two main forms: A late-onset form known as sporadic AD, which is more common; and an early-onset or familial form with 5% of all AD cases [ 31 ]. It has been seen that in individuals suffering with Down’s syndrome (or trisomy 21), there is an increased risk of familial AD, as they are carriers of an extra chromosome 21 where the gene responsible for the formation of APP is present ( Figure 1 ).

Amyloid precursor protein (APP) cleavage in normal (non-amyloidogenic) and AD (amyloidogenic) pathways.

Similarly, a mutation in some of the many genes including PSEN1 and PSEN2, which code for APP, Presenilin1 and Presenilin2 which are also the two subunits of γ-secretase, have been recognized as the causative genes for familial AD [ 20 ]. These mutations cause an enhanced production of Aβ, with the mutations on PSEN1 particularly leading to an increase in formation of Aβ (1–42). The apolipoprotein (ApoE), which is involved in the clearance of Aβ, is a major genetic risk factor associated with late-onset AD [ 32 , 33 ]. There are three categories in which the mutations have been divided: The N-term mutation occurring at the cleavage site for β-secretase, the C-term mutation at the cleavage site for γ-secretase, and the mutation occurring in the mid-domain Aβ region. The mutations that occur at γ-secretase cleavage site can increase the ratio of Aβ1–42/Aβ1–40 and alter the position of cleavage. There is an increase in the rate of proteolysis of APP by β-secretase due to the mutations at β-secretase cleavage site. While the mutations occurring at the mid-domain of Aβ region in APP lead to an increase in the Aβ propensity for formation of oligomers and fibrils that disrupt the Aβ assembly. Many studies have reported mutation at the γ-secretase processing site of APP [ 34 , 35 , 36 , 37 , 38 ]. More than the protofibrils and fibrils, it is the oligomers that are found to be more toxic for the brain cells ( Figure 1 ). This is because the oligomers are capable of permeating the cellular membranes causing cellular dysfunction and death.

The cascade of Aβ involves a number of factors and modulators that have an essential role each to play. Metal ions such as iron, zinc and copper are found to be present in the amyloid plaques and are involved in creating conditions of oxidative stress, as well as in the modulation of aggregation process by binding to Aβ [ 39 ]. These ions function by acting on the kinetics or thermodynamics to affect the structural morphology of the aggregates formed. The amyloid aggregates that have metal ions entrapped within them have been found to be highly toxic as they can cause the production of reactive oxygen species (ROS) which have a deleterious effect on both the Aβ peptide and the biomolecules in the vicinity [ 40 ]. Release of inflammatory factors like reactive oxygen species (ROS), nitric oxide synthase (NOS) and prostaglandins is stimulated bringing about the death of nerve cells [ 41 ].

There are drugs that serve as beta and gamma secretase inhibitors, including Elenbecestat (E2609), verubecestat (MK-8931) and Semagacestat [ 42 ], but none of them have cleared all the steps of clinical trials [ 43 ]. Similarly, beta secretase modulators also failed due to their unsafe use to patients. The cleavage of APP by α- and γ-secretase produces sAPPα (soluble amino terminal ectodomain of APP), a larger C83 fragment (carboxy terminal) and a smaller fragment p3. This pathway does not give rise to amyloid beta (Aβ) production. The cleavage of APP by β-secretase (BACE1) and γ-secretase produces sAPPβ, C99, AICD (APP intracellular domain) and leads to the formation of Aβ [ 44 , 45 ].

2.2. The Tau Hypothesis

Tau is present in axons and dendrites and it regulate microtubules function [ 46 , 47 , 48 , 49 ]. The biological functioning of Tau is regulated by the level of its phosphorylation in the brain. Tau generally contain 2–3 mole of phosphates per mole of protein, but, in the case of AD brain, it contains more phosphates [ 50 , 51 ]. An excessive or hyper phosphorylation of microtubule-associated protein, Tau in case of AD, leads to its transformation from normal adult Tau to a paired helical filament (PHF-tau) of it, impairing its ability to bind to the microtubules stably [ 52 , 53 ]. This is a result of mutations that cause tau to aggregate and attain an insoluble structure, as opposed to their normal soluble structure. The insoluble state leads to enormous destruction of cytoplasmic functions of the nerve cells and a disruption in axonal transport, ultimately leading to dementia and neuronal death [ 54 ]. Neuronal cell death mediated by tau along with hyperphosphorylation also requires the activation of glycogen synthase kinase 3β (GSK3-β). Previous studies have reported that inhibition of GSK3-β decreases tau phosphorylation [ 55 , 56 ] ( Figure 2 ). The tau pathology states that the formation of neurofibrillary tangle (NFT) spreads to various parts of the brain by following a stereotyped pattern of six pathological stages, wherein the first two stages the cognition of the patients is impaired.

Interactions occurring between amyloid beta (Aβ), hyperphosphorylated tau, glycogen synthase kinase 3β (GSK-3β) and ceramides (cer).

There is a neurodegenerative “triad” of cellular changes that has been revealed via the microscopic analysis of different models of AD animals as well as AD patients, which affects the disease development. This triad comprises of: (a) A density decrease accompanied by a change of shape of the dendritic spines, which are the postsynaptic excitatory input site of most neurons; (b) neuronal cell loss in specific regions of the brain; and (c) a subset of neurons that undergo dendritic simplification. Over the years, the emergence of different events of the neurodegenerative triad may occur at different points of time with the progression of AD [ 57 ]. Studies on organotypic cultures and animal models have shown that the loss of dendritic spines and changes in synapse begin to surface very early in the disease. These changes; however, can be reversed if the amount of Aβ is reduced and the cAMP/PKA/CREB signaling pathways are restored [ 58 ] ( Figure 2 ).

Loss of neurons and dendritic simplification are events that are found to appear later in the disease, which suggests that the aspects of neurodegenerative triad dependent on tau are characteristic of further disease progression. It is clear that the loss of neurons is an irreversible event, the reversibility of dendritic simplification; however, is yet to be established. Since it is known that the loss of synapse and dendritic simplification is caused due to a disruption of the cytoskeleton, drugs capable of modulating dynamics of the cytoskeleton, and the microtubular network dynamicity in particular, can serve as therapeutic options for the counteraction of tau-mediated changes [ 59 ]. In tau knockout animal models, it was observed that there was no major effect on the development and function of brain. This led to an increased interest in the development of such strategies that were directed to tau, as they would have fewer side effects as compared to the drugs that were directed on APP and Aβ, which are involved in numerous biological processes. There are six isoforms of tau present in the CNS that are produced by the alternative splicing of three axons [ 60 , 61 ]. Any error in the splicing of tau, particularly an increased formation of longer isoforms of tau can lead to tauopathies. PHF-tau is phosphorylated at its serine and threonine residues several times [ 62 , 63 , 64 , 65 ].

Tau undergoes a number of post-translational modifications such as ubiquitination, acetylation, methylation and O-glycosylation. Studies on mouse models have shown that tau turnover was reduced and tau aggregation was increased through the acetylation of tau at Lys174, which was identified as an early modification in the brains of AD patients [ 66 ]. A number of interaction partners of tau that could be of functional importance have been found apart from microtubules, such as annexin A2, a membrane associated protein contributing to the axonal localization of tau; fyn, a non-receptor tyrosine kinase of the src-family involved in post-synaptic Aβ toxicity; and a primary tau phosphatase, protein phosphatase 2A.

GSK-3β is the major kinase involved the phosphorylation process of tau. With the aid of GSK-3β, the intracellular aggregation of Aβ occurs that might also contribute to the hyperphosphorylation of tau. Additionally, the Aβ aggregation acts on sphingomyelinases (SM; enzymes involved in the degradation of sphingomyelin) affecting ceramide production. The ceramides produced act on β-secretase (enzyme involved in proteolytic cleavage of APP) leading to increased Aβ production. Presenilin and brain-derived neurotrophic factor (BDNF) are responsible for modulating these interactions by the P13-K/Akt signaling pathway. P13-K causes activation of the Akt/protein kinase B, which further causes phosphorylation of GSK-3β inducing its inactivation and; thus, downregulating phosphorylation of tau.

2.3. The Cholinergic Hypothesis

It is the oldest known hypothesis which forms the basis of most of the drugs available in the market today [ 67 ]. According to this hypothesis, there is a reduced rate of production and transportation of the neurotransmitter acetylcholine in the brains of AD-affected individuals [ 68 ]. This neurotransmitter is used by all the cholinergic nerve cells and has an important role in the peripheral and central nervous systems, as it is used by all pre and post-ganglionic parasympathetic nerve cells and also all the pre-ganglionic sympathetic nerve cells. Studies have shown that the cholinergic system is a crucial contributor to the learning and memory processes [ 69 , 70 , 71 , 72 , 73 ]. In AD, the cholinergic neurons forming the nucleus basalis of Meynert are specifically degenerated, which causes memory loss seen in the AD patients [ 74 , 75 , 76 , 77 , 78 ]. The nucleus basalis region of a healthy adult brain contains about 500,000 cholinergic neurons, whereas a mere 100,000 remain in advanced AD patients [ 79 ]. There is a major decrease in the transcription of enzyme choline acetyltransferase (ChAT) in the remaining cholinergic nerve cells, leading to diminished activity of ChAT and the condition of dementia. It has also been found that the release of ACh in the forebrain can be regulated by stress conditions. A disruption in its transmission process is capable of affecting all aspects of cognition, the cortical and hippocampal information processing and behavior. Any change from the normal in the cholinergic inputs to the brain cortex leads to an impairment in attention and cognitive functions such as the processing of instructions required for decision making.

Moreover, it has been found that memory and knowledge encoding is impaired upon the blockage of CA3 cholinergic receptors. A reduction in the cholinergic neurons and the resulting impaired dopaminergic transmission has also been considered as a major factor related to psychiatric symptoms in AD. This hypothesis can be supported by the fact that there is an increase in the efflux of dopamine in nucleus accumbens as seen in M4 knockout mice. The loss of cholinergic neurons is not only found in the case of AD, but also in a number of other neurodegenerative disorders including PD, HD and ALS where a significant decrease in the activity of ChAT is seen [ 80 ]. The cholinergic synapses are severely affected by Aβ, which can be correlated to the cognitive decline. The hippocampal synaptic transmission is changed with respect to changes in the expression of synaptophysin, a major presynaptic vesicle protein p38, which correlates highly with the neuropathology and memory loss observed in AD patients. A severe deficit in basal synaptic transmission (~40%) was recorded upon electrophysiological studies in the hippocampal region of mutant APP mice.

Cholinergic neurons play a significant role in promoting memory and cognitive functions, as proven via experimentation studies on rat models using cholinergic antagonists which showed cognitive damage in the rats [ 81 , 82 ]. The coupling of M1 muscarinic receptors to G-proteins is damaged in the neocortex of AD patients. It has been demonstrated that the extent of this uncoupling of M1 and G-protein is linked to the graveness of cognitive symptoms in AD. Further, a shift in the processing of APP towards the non-amyloidogenic pathway occurs when muscarinic receptors are activated. M1 receptor signaling is also known to be affecting a number of hallmarks in AD, such as cholinergic deficiency, Aβ and tau pathologies and cognitive dysfunctions. M1 receptor activation can activate PKC and inhibit GSK3-β, which can lead to a significant reduction in tau hyperphosphorylation. AF267B, a known M1 agonist, is capable of rescuing the decline in cognition via a decrease in Aβ42 and abnormalities associated with tau in the cortex and hippocampus, as seen in an AD mouse model. These findings and several other studies have produced the option of M1 acetylcholine receptor agonists as potential therapeutic tools for treating AD. Acetylcholinesterase (AChE) inhibitors, like Donepezil, work by decreasing the hydrolysis of Ach and improving memory and cognition [ 83 ], while Rivastigmine serves an additional function of blocking not only acetylcholinesterase, but also butyl cholinesterase to increase the chances of managing AD ( Figure 3 ). Similarly, there is Galantamine which is an effective drug working as an AChE inhibitor on the same mechanism.

A diagrammatic representation of the neuroprotective activity of Acetylcholine esterase (AChE) inhibitors (such as Donepezil and Galantamine).

They stimulate nAchr (nicotinic acetylcholinesterase receptor) through a site other than the AChE binding site under normal condition. α7 nAChR, when stimulated by these drugs, causes the activation of PI3K (phosphatidylinositol 3-kinase) due to the activation and association of Jak2 (janus activated kinase 2) with the non-receptor type tyrosine kinase Fyn. Activation of PI3K activates Akt by phosphorylation (Akt-p). Nicotine treatment increases the level of Akt-p. It further increases the Bcl-2 expression level, preventing the death of nerve cells. The hypoactivation of α7 nAChR decreases activation of PI3K and Jak2. This increases GSK-3β enzyme activity, which increases phosphorylation of tau proteins causing neuronal death [ 84 ].

2.4. The Dendritic Hypothesis

This hypothesis focusses on the degeneration of dendrites accompanied with their structural and functional disturbances caused in AD. The activation of N-methyl-d-aspartate receptor (NMDAR) finds a major implication in Alzheimer’s disease, which has been studied extensively over the last few years. NMDARs are crucial for the processes of neurotransmission and synaptic plasticity in the brain [ 85 , 86 , 87 , 88 ]. Glutamate is the most abundantly present excitatory neurotransmitter found in the mammalian CNS. Ligand-gated ionotropic glutamate receptors (iGluRs) play a pivotal role in the excitatory neurotransmission, and a disruption in their normal signaling process is associated with a number of neuropathological diseases like AD, PD, HD and multiple sclerosis, which makes them important therapeutic drug targets [ 89 , 90 ]. There are three subfamilies of iGluRs, which are: a-amino-3-hydroxy-5-methyl-4-isoxasolepropionic acid receptors (AMPARs), kainate receptors and NMDARs. Owing to certain unique properties associated with it, the NMDAR is distinct from the other two iGluRs in the voltage-dependent activation [ 90 , 91 ]. The NMDAR has high permeability to calcium ions (Ca2+) and its ligand-gated kinetics is relatively slow, which makes it crucial in synaptic functions. The Ca2+ channel of NMDAR remains blocked by Mg2+ at the resting membrane potential (−70 mV), while this blockade is removed during the long term potentiation (LTP), allowing a prolonged and strong release of glutamate from the presynaptic terminal [ 92 , 93 ]. This leads to the activation of AMPARs and subsequently the depolarization causes Mg2+ removal from the NMDAR channel and a Ca2+ influx. This also triggers the activation of a Ca2+/calmodulin-dependent protein kinase II (CaMKII)-mediated signaling cascade, which causes an increase in the synaptic strength. A moderate activation of NMDARs causes a moderated increase in the postsynaptic Ca2+ and a trigger of phosphatases mediating long term depression (LTD) [ 94 ].

There are two types of membrane NMDARs: Synaptic and extrasynaptic. By the activation Ca2+ dependent transcription factors, such as cyclic-AMP response element binding protein (CREB) and the suppression of apoptotic pathway and caspases, the synaptic NMDAR helps in promoting the expression of survival gene [ 95 ]. The extrasynaptic NMDAR, on the contrary, is involved in glutamate excitotoxicity and cell death. Its responses are strongly linked with the physiological changes in AD. The activation of the two NMDARs occurs by two different endogenous coagonists: D-serine for synaptic NMDAR and glycine for extrasynaptic NMDAR. The signaling pathway mediated by extrasynaptic NMDAR is known to antagonize the cell survival pathway via CREB inactivation and FOXO (forkhead box) transcription factor activation which promotes pro-apoptotic and oxidative stress signaling [ 96 ]. AD affects the NMDAR coagonist levels. As the binding of coagonist D-serine or glycine is needed for the complete activation of NMDARs by glutamate, the coagonists serve an essential modulatory role in the functioning of NMDAR. One of the FDA approved drugs for AD, memantine, works as an NMDAR antagonist and targets against the extrasynaptic NMDAR. The level of Ca2+ entering through NMDAR that exceeds the pathological normal [ 97 ] determines the level of toxicity produced. It results in a gradual loss of synaptic plasticity and neuronal death eventually that can be clinically correlated to a decline in memory and cognition in AD patients. Not only is the electrophysiological functioning of NMDARs directly modulated by Aβ, an elevation in the levels of synaptic currents and collateral toxicity mediated by NMDARs is also brought about by Aβ in AD. NMDAR antagonists such as MK-801 serve as the blockers or attenuators of the same.

Since NMDARs also play an essential role in cell survival, a balance in their level of signaling is of utmost importance, such that it is sufficient for neuronal survival and at the same time, does not bring about neurodegeneration as in the case of AD [ 98 ]. The amount of glutamate available for signaling depends upon its uptake and recycling system, which was found to be severely compromised in AD. A study on an AD patient revealed that there is severe reduction in the capacity of glutamate transporter and protein expression [ 99 ]. The expression of presynaptic proteins, including syntaxin and synaptotagmin, which comprise the neurotransmitter release machinery, are known to be greatly reduced due to Aβ. Aβ can interact with NMDARs indirectly via such synaptic proteins as PSD95 [ 100 ]. The deficiency in presynaptic proteins leads to a compromised availability of glutamate, thus producing excitotoxicity, an effect often seen in degenerating nerve cells. The activation of N-methyl-D-aspartate (NMDA) receptor by the amyloid beta and prion proteins, in addition to the activation of Fyn by prion protein and Fyn tyrosine kinase-metabotropic glutamate receptor 5 complex (FynmGluR5), results in the decrease of NMDA receptors [ 101 ]. Fyn, upon overstimulation, causes cognitive damage and synaptic losses leading to the disease condition [ 28 ]. Memantine, a drug that functions as an NMDA receptor blocker, has been approved by FDA and is currently in use, although relatively preferred less in comparison to AChE inhibitor drugs.

2.4.1. Wnt/Beta-Catenin Signaling

The low density lipoprotein receptor-related protein 6 (LRP6) is a major Wnt co-receptor required to activate the Wnt/β-catenin pathway on the cell surface. LRP6 is strongly related to the signaling pathway of glucose and lipid metabolism [ 102 , 103 , 104 ]. The Wnt/β-catenin pathway is responsible for the regulation of a number of significant cellular functions, such as cell growth and proliferation, differentiation and migration. A dysregulation in this pathway has been found to play an important role in AD pathogenesis. The pathway is activated when Wnt proteins bind to the Frizzled (Fzd) receptor family’s cysteine rich domain and Wnt co-receptor LRP6. Studies have shown that susceptibility of neurons to death induced by amyloid beta increases with a decrease in the Wnt/β-catenin signaling, whereas the same Aβ-induced neuronal death can be prevented by the activation of this signaling [ 105 , 106 ]. Whether there is occurrence of neurogenesis in an adult human brain has been a much debated topic. There is evidence supporting the occurrence of neurogenesis in the hippocampal region of the human brain which experiences a sharp decline in the case of AD.

Studies show that the Wnt/β-catenin signaling has a key role in the regulation of neurogenesis in adult hippocampus, as it is activated by the Wnt7a gene at multiple steps of neurogenesis along with other specific genes controlling the neuronal cell cycle and differentiation processes [ 105 , 107 ]. It was found that, in aged mice, the Wnt proteins secreted by astrocytes decrease, causing decreased Wnt/β-catenin signaling, decrease in the level of survivin (responsible for mitotic regulation) in neural progenitor cells (NPC) and an impaired neurogenesis [ 108 ]. The activation of the Wnt/β-catenin signaling pathway determines the activation of survivin and transcription factors, such as NeuroD1 and Prox1, which are involved in the generation of hippocampal granule cells [ 109 ]. Furthermore, this signaling pathway plays an essential role in maintaining the synaptic plasticity. The Wnt proteins are involved in synapse formation and the pre- and post-synaptic modulation of neurotransmission. LRP6 helps in the in vivo and in vitro development of excitatory synapse and its deficiency leads to abnormal synapse and cognition, as found in aged mice models [ 107 , 110 ]. Other functions associated with the activation of LRP6-mediated Wnt/β-catenin pathway include the function and formation of blood–brain barrier (BBB), by activating the signaling in endothelial cells of the BBB, and inhibition of β-plaque formation via inhibition of transcriptional expression of β-site APP cleaving enzyme (BACE1) [ 111 , 112 , 113 , 114 ]. Interaction of LRP6 with APP lowers the production Aβ and the suppression of tau phosphorylation via suppression of the GSK3β kinase activity.

2.4.2. GSK3-β Activity

Glycogen synthase kinase-3 (GSK-3) is a serine-threonine kinase that functions as a key regulator in many biological pathways, some of which have their implications in AD. It has two isoforms, GSK3-α and GSK3-β, each encoded by a different gene. The GSK3-β is found in abundance in the CNS, with the level of its expression increasing with age, and its activity superseding the normal in case of AD patients [ 56 ]. The over activation of this kinase is linked with the deposition of amyloid beta, memory impairment and plaque-related inflammatory responses mediated by microglia. The cleavage of APP in the non-amyloidogenic pathway that involves α- and γ-secretases has three members ofthe α-disintegrin and metalloproteinase (ADAM) family (ADAM-10, ADAM-17, and ADAM-9) forming the α-secretase complex [ 113 , 115 ]. GSK3-β is known to inhibit the activity of ADAM and thus downregulate the activity of α-secretase complex. Amongst the proteins constituting the γ-secretase complex, the function of presenilin (PSEN) 1 is affected by GSK3-β [ 116 ]. Since APP and PSEN1 are both substrates of GSK3-β, it interferes with the production of Aβ at the step of APP cleavage by γ-secretase [ 117 ].

The signaling of this kinase is found to be activated by Aβ, as its inhibition via phosphorylation is prevented by Aβ in transgenic AD models of animals. Similarly, an increased GSK3-β activity was observed in the brains of AD patients. A reduction in Aβ production, as well as Aβ-induced neuronal toxicity, was seen upon the inhibition of GSK3-β in mice models of AD. BACE1 mediates the APP cleavage by NF-kB signaling mechanism [ 112 , 113 , 114 ]. The expression of BACE1, which is found to be increased in AD patients, can be downregulated upon GSK3-β inhibition [ 112 ]. GSK3-β is known to phosphorylate at least 36 different residues in the tau protein, with the major sites identified to be Ser199, Thr231, Ser396, Ser413 and other sites of moderate phosphorylation including Ser46 and Ser202/Thr205 [ 118 , 119 , 120 ]. Apart from GSK3-β, CDK-5 and PKA are two other kinases associated with microtubules and tau protein. Tau is a microtubule-associated protein (MAP) that functions as a regulator of microtubule formation and its stability [ 121 , 122 ]. A combined action of GSK3-β and CDK-5 is required for the formation of paired helical filaments of tau (PHF tau) [ 123 ]. This form of the tau protein is insoluble and can aggregate and deposit inside the nerve cells leading to the formation of neurofibrillary tangles (NFTs). The PHF tau is unaffected by the action of proteases or phosphatases. Studies have shown that GSK3-β is activated by an elevation in oxidative stress, neuroinflammation and apoptotic cell death that is brought about by hyperphosphorylated tau [ 117 ]. Along with neuronal death and hyperphosphorylation in tau, GSK3-β overexpression has been found to cause a failure in mice to perform the Morris water maize test [ 124 ]. It causes increased apoptosis in some particular areas of the brain, including the hippocampus which controls memory and cognition and is severely affected in AD. However, it was seen that these effects were reversed and tau hyperphosphorylation was reduced upon restoration of GSK3-β to the normal levels. GSK3-β can regulate the stability of axons directly by interacting with microtubules, owing to its capacity to phosphorylate numerous MAPs [ 125 ].

The MAP-2 and tau phosphorylated by GSK3-β are deprived of their affinity towards microtubules, making them unstable in nature. Failure in axonal transport results, which adds significantly to the pathology of AD [ 125 ]. GSK3-β is known to be involved in the metabolism of choline and regulation of choline acetyltransferase (ChAT), as well as acetylcholinesterase [ 126 ]. A reduction in phosphorylation of Ser9 of GSK3-β has shown to cause a loss of cholinergic nerve cells from the basal forebrain and hippocampal area, and an enhanced phosphorylation of tau [ 118 , 119 , 120 ]. GSK3-β plays an important role in the process of inflammation, as it can regulate the process in a positive manner by promoting the activity of pro-inflammatory cytokines [ 127 ], while lowering anti inflammatory cytokines activity. Over the recent years, a number of GSK3-β inhibitors have been developed, both ATP-and non-ATP-competitive types [ 128 ]. Since the non-ATP-competitive GSK3-β inhibitors prove to be more sensitive, selective and less toxic in nature, they are preferred more than the ATP-competitive type (Indirubin).

2.5. The 5-HT 6 Receptor Hypothesis

It has been found in recent studies that the inhibition of antagonists at serotonin type 6 (5-HT 6 ) receptor can improve cognition in AD [ 129 , 130 , 131 , 132 ]. The injection of 5-HT 6 receptor antagonists in rodent models led to significant cognitive improvement [ 133 , 134 ]. This receptor is also found to be involved in amyloid protein formation and the signaling of Fyn [ 135 ]. These receptors can; therefore, play a major role in AD treatment as the inhibitors can also stop Fyn activation and deposition of amyloid. All the current drugs, apart from AChE inhibitors, have adverse effects associated with them which fails them in phase 2 or 3 of clinical trials [ 136 ]. It is worth noting that Dimebon (latrepirdine, also known as Dimebolin) was initially developed as an antihistamine drug. For 5-HT 6 receptors (ki = 34 nM), this compound shows strong affinity. After a very promising phase 2 review, Dimebon gained widespread attention as a possible treatment for AD [ 137 ]. A more recent multinational phase 3 research; however, has shown no changes [ 138 ].

3. Nanotechnology-Assisted Drug Delivery Strategies for AD

All the drugs currently approved for the treatment of AD are available as oral formulations, barring Rivastigmine which also has a transdermal patch available [ 28 , 139 ]. Since the drugs need to reach the CNS in order to control the progression of disease or its symptoms, a much higher dose needs to be consumed because of the large fraction of drug that is lost along the way in GI tract and metabolism in the hepatic region [ 28 , 139 ]. Furthermore, the drug needs to bind to serum albumin in the blood stream in order to sustain a decent half-life, before it finally reaches the BBB [ 140 , 141 ]. Consuming these dosages leads to the patients suffering with side effects like nausea and diarrhea and reducing their compatibility. Nanotechnological advancements in the recent years have provided us with the option of nanoparticles that help in overcoming these hurdles in drug delivery, particularly in improving the side effects by reducing the dosage and in easily traversing across the BBB to provide targeted delivery of the drug. The size of nanoparticles falls in the range of 1 to 100 nm so as to permeate the BBB [ 140 , 141 ].

They are formulated in such way that makes them nontoxic, biodegradable and target-specific in nature. These types of nanosystems may effectively hold and distribute drugs and other neuroprotective molecules to the brain in the sense of treating AD [ 142 , 143 , 144 ]. The intranasal route plays a role in overcoming the BBB and targeting the drugs directly to the brain [ 145 , 146 , 147 , 148 , 149 ]. However, in order to optimize pharmacotherapy in patients with AD, nasal, dermal, and intravenous routes may be used to administer nanodevices to target the brain moving through BBB to improve bioavailability, pharmacodynamic properties and decrease the adverse effects of these medications [ 150 , 151 , 152 ]. The most common mechanisms of nanoparticle transportation include endocytosis, like receptor-mediated endocytosis, phagocytosis and pinocytosis, with receptor-mediated endocytosis being the most preferred method. The incorporated drug is delivered at the target site by diffusion and erosion or degradation processes. Some of the nanoparticles most often used are liposomes, polymeric nanoparticles, micro- and nanoemulsions and dendrimers.

3.1. Liposomes

These are bilayered phospholipids that are amphiphilic in nature (i.e., capable of transporting both hydrophilic and lipophilic drug molecules) [ 153 , 154 ]. Some antibodies have been proposed to inhibit the spread of Tau pathology by microglial phagocytosis of the antibody–Tau complex and to promote the clearance of lysosomal Tau in neurons after endosomal uptake [ 155 , 156 ]. The main components of liposomes include phosphatidyl choline, sphingomyelin and glycerophospholipids. Liposomes contain cholesterol that helps in maintaining its stability inside the serum. Their size ranges typically from 50 to 100 μm. The drug is encapsulated inside a lipid bubble which aids in its protection from degradation by enzymes and retains its effectiveness [ 157 ]. Liposomes have been studied by researchers extensively over the years.

3.2. Polymeric Nanoparticles

These are nanoparticles composed of synthetic or natural polymers, having their size in the range 1–100 nm [ 158 ]. The hydrophilic or hydrophobic nature of PNPs depends on the nature of the part forming its outermost layer. The mechanism of their transport to the target site can either be via receptor-mediated endocytosis or transcytosis of endothelial cells. The absorption of drugs can be enhanced by coating the PNPs with antibodies or PEG (polyethylene glycol), especially while delivering the drug via the intranasal route. Poly (n-butyl cyanoacrylate) loaded with the drug Rivastigmine showed improved drug delivery in case of AD when coated with polysorbate-80 [ 28 ]. In an experimental AD model, Aβ1-42 monoclonal antibody-decorated nanoparticle-based therapy against AD leads to complete correction of the memory defect [ 159 ]. With unique quantum properties that are promising to diagnostic and imaging purposes, nanoparticles can be prepared [ 160 ]. Micelles, nanogels, dendrimers and nanocapsules can be formulated as polymeric NPs [ 161 , 162 ].

3.3. Micro- and Nanoemulsions

These types of nanoparticles fall under the category of surfactant-based systems. The size of microemulsions ranges between 10 to 140 nm, while that of nanoemulsions lies around 100 nm [ 163 ]. These systems are also called oil-in-water (O/W) heterogeneous systems as they are formed by the dispersion of oil in water or any other aqueous medium. Hyaluronic acid-based nanoemulsion of curcumin and resveratrol used by Nasr showed promising results when delivered via the intranasal route to the brain. The preparation of the nanoemulsion was done using spontaneous emulsification method [ 139 ]. As a possible carrier of memantine for a direct nose-to-brain transmission, the produced nanoemulsion could be used [ 164 ].

3.4. Dendrimers

These are polymeric branched, globular molecules also known as cascade molecules or arborols. They derive their name from their structural nature, which is to ramify progressively while originating from a core, similar to the behavior of the branches of a tree. Divergent and convergent are two methods of production of dendrimers. They offer a high drug loading capacity, including both the inner cavity and the outer surface of the dendrimer. The size of dendrimers can be easily regulated through careful selection of the monomers and the degree of polymerization. The only limitation is the issue of toxicity that is often faced when using these nanoparticles shown in Figure 4 .

Diagrammatic representation of drug delivery options using nanotechnology for therapeutic purposes in AD.

Polyamidoamines (PAMAMs), which are biocompatible, nonimmunogenic, and hydrophilic in nature, are the most used dendrimers in drug delivery. The nucleus of these dendrimers was composed of branching hydrophobic molecules of ethylenediamine and methylacrylate terminated by groups of carboxyl and amine. PAMAM dendrimers are used in drug delivery as carriers [ 165 ], diagnostic agents [ 166 ], gene transfection [ 167 ] and boron neutron capture treatment for metastatic brain tumors.

4. Conclusions

There are several hypothesis for the neurodegeneration process; however, the lack of availability of in vivo models makes the recapitulation of AD in humans impossible. Moreover, the drugs currently available in the market serve to alleviate the symptoms and there is no cure for the disease. There have been two major hurdles in the process of finding the same—the inefficiency in cracking the complexity of the disease pathogenesis and the inefficiency in delivery of drugs targeted for AD. This review discusses the different drugs that have been designed over the recent years and the drug delivery options in the field of nanotechnology that have been found most feasible in surpassing the blood–brain barrier (BBB) and reaching the brain.

Acknowledgments

M.Z.M. was financially supported by the Department of Health and Research, Ministry of Health and Family Welfare, Government of India under young scientist FTS No. 3146887.

Author Contributions

D.-K.K. and M.Z.M. conceived the idea. M.A., M.K.H. and M.R.A. wrote the manuscript. D.-K.K. and M.Z.M. critically revised the manuscript. All authors have read and agree to the published version of the manuscript.

This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2018R1D1A1B07040673, No. 2014R1A5A2010008).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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• Open access
• Published: 30 January 2018

Alzheimer’s disease hypothesis and related therapies

• Xiaoguang Du 1 ,
• Xinyi Wang 1 &
• Meiyu Geng 2  

Translational Neurodegeneration volume  7 , Article number:  2 ( 2018 ) Cite this article

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause for dementia. There are many hypotheses about AD, including abnormal deposit of amyloid β (Aβ) protein in the extracellular spaces of neurons, formation of twisted fibers of tau proteins inside neurons, cholinergic neuron damage, inflammation, oxidative stress, etc., and many anti-AD drugs based on these hypotheses have been developed. In this review, we will discuss the existing and emerging hypothesis and related therapies.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, which is the most common cause for dementia and imposes immense suffering on patients and their families. According to the World Alzheimer Report 2016, there are currently about 46.8 million people suffering with AD worldwide. The ageing of world population will further compound this problem and lead to a steep increase in the number of AD patients. The numbers of AD patients are expected to double nearly every 20 years, and thereby the population of AD will reach 74.7 million in 2030 and 131.5 million in 2050 [ 1 ]. AD has become the third major cause of disability and death for the elderly, only after cardiovascular and cerebrovascular diseases and malignant tumors.

However, only five drugs have been approved by the FDA to treat AD over the past hundred years since the first AD patient was diagnosed. Not only that, these approved drugs including cholinesterase inhibitors, N-methyl-D-aspartate (NMDA) receptor antagonist or their combination usually provide temporary and incomplete symptomatic relief accompanied with severe side effects. The marginal benefits were unable to slow the progression of AD. Thus, developing drugs for more effective AD treatment is in urgent need.

Current hypothesis about AD and anti-AD drug development

AD is a complicated disease involving many factors. Due to the complexity of human brains, the lack of reasonable animal models and research tools, the detailed pathogenesis of AD is still unclear so far. Many hypotheses about AD have been developed, including amyloid β (Aβ), Tau, cholinergic neuron damage and oxidative stress, inflammation, etc. Thus, many efforts have been done to develop anti-AD drugs based on these hypotheses.

Aβ cascade hypothesis

Extracellular deposits of Aβ peptides as senile plaques, intraneuronal neurofibrillary tangles (NFTs), and large-scale neuronal loss were the main pathological features of AD. Thus, Aβ peptides have long been viewed as a potential target for AD which dominated new drug research during the past twenty years [ 2 ]. The most direct strategy in anti-Aβ therapy is to reduce Aβ production by targeting β- and γ-secretase [ 3 ]. Safety issues are the overriding problem. For targeting γ-secretase, undesirable side effects are inevitable due to its physiological substrates, eg. the Notch signaling protein [ 4 , 5 , 6 , 7 ], which is essential in normal biological process. Similarily, targeting β-secretase is also challenged for the side effects such as blindness and the large catalytic pocket [ 8 ]. More importantly, in sporadic AD cases, the majority of AD patients do not necessarily have over-producted amyloid precursor protein. Besides, Aβ isoforms could also serve as endogenous positive regulators for neurotransmitter release at hippocampal synapses [ 9 ]. Thus, inhibiting Aβ production may encounter many challenges.

Aβ clearance by immunotherapy is the alternative choice. For active Aβ-immunotherapy, although the first active AD vaccine (AN1792) developed by ELAN showed some beneficial effects such as less cognitive decline, it was suspended owing to serious side effect, or meningoencephalitis [ 10 , 11 , 12 ]. Also, the passive immunotherapy did not do much better than active immunotherapy. Several antibodies targeting Aβ have failed in clinical trials, including bapineuzumab (Pfizer/Johnson & Johnson) [ 13 , 14 ], Crenezumab (Genentech) [ 15 , 16 ], solanezumab (Eli Lilly) [ 16 , 17 , 18 ] and ponezumab (Johnson & Johnson /Pfizer) [ 19 , 20 , 21 ]. In addition, although passive immunotherapy could overcome some problems of active immunotherapy, there were still inevitable side effects such as amyloid-related imaging abnormalities [ 22 ]. Likewise, the small molecule Aβ binder scyllo-inositol [ 23 ] and tramiprosate [ 24 , 25 , 26 ] also failed in clinical trials. These failures even cast more doubts on the Aβ theory [ 27 ]. Actually, the strategy of targeting only a single functional subregion of Aβ may partly account for these failures [ 27 , 28 ]. Furthermore, immunotherapy may influence the human immune system, which might cause beneficial or detrimental consequence (such as side effects). However, every cloud has a silver lining. A phase Ib trial of aducanumab (Biogen) showed a positive correlation between brain Aβ levels and disease exacerbation as measured by Clinical Dementia Rating [ 29 , 30 , 31 ]. Even the failed phase III EXPEDITION3 trial of solanezumab (Eli Lilly) still demonstrated better performance in Clinical Dementia Rating Sum of Boxes and beneficial impacts on Mini-Mental State Examination and Activities of Daily Living [ 17 , 18 , 32 , 33 ]. Thus, despite all kinds of problems, immunotherapy may still be the better approach to modify the extent of neurodegeneration in AD currently [ 34 ].

In fact, the original amyloid cascade hypothesis was that “Aβ is the causative agent in Alzheimer’s Disease pathology, and that neurofibrillary tangles, cell loss, vascular damage, and dementia follow as a direct result of this deposition” [ 35 ]. After decades of research, although the bulk of data still supports a role for Aβ as the primary initiator of the complex pathogenic cascade in AD, more and more evidences indicate that Aβ acts as a trigger in the early disease process and appears to be necessary but not sufficient in the late stage of AD [ 36 ]. Especially, recent rapid progresses in understanding on toxic amyloid assembly and Aβ metabolism associated systemic abnormalities will provide fresh impetus and new opportunities for this interesting approach [ 37 ].

Tau hypothesis

Neurofibrillary tangles, another intracellular hallmark of AD, are composed of tau. Tau is a microtubule-associated protein working as scaffolding proteins that are enriched in axons. In pathological conditions, tau aggregation will impair axons of neurons and thus cause neurodegeneration. After numerous failures of Aβ-targeting drugs for AD, more interests are turning to explore the therapeutic potential of targeting tau, particularly as studies of biomarkers suggest that tau pathology is more closely linked to the progression of AD [ 38 ].

Tau undergoes many modifications, including phosphorylation, arginine monomethylation, lysine acetylation, lysine monomethylation, lysine dimethylation, lysine ubiquitylation and serine.

O-linked N-acetylglucosamine (O-GlcNAc) modification [ 39 ]. Under pathological conditions, increasing of tau hyperphosphorylation will render the protein aggregation-proned, reduce its affinity for microtubules, and thereby influence neuronal plasticity. Consequently, strategies to target tau involve blocking of tau aggregation, utilizing tau vaccinations, stabilizing microtubules, manipulating kinases and phosphatases that govern tau modifications. However, most of these efforts have failed in clinical trials. For Tau aggregation blockers, TRx0237 failed to show treatment benefits in phase III trials [ 40 ]. As for vaccinations, tau-targeted active vaccines (ACI35 and AADvac-1) and passive vaccines (RG6100 and ABBv-8E12) are currently in phase I and II clinical trials [ 41 , 42 ]. Intravenous immunoglobulin (IVIG), the only passive vaccine in phase III clinical trials, failed to meet the primary end points in patients with mild-to-moderate AD [ 42 ]. Other tau-targeting strategies for AD, including stabilizing microtubules and manipulating kinases and phosphatases, have just been tested in preclinical studies.

In general, tau-targeting therapies remain challenging because of incomplete understanding of AD, lack of robust and sensitive biomarkers for diagnosis and response-monitoring, and the obstruction of blood-brain barrier.

Inflammation hypothesis

Reactive gliosis and neuroinflammation are hallmarks of AD. Microglia-related pathways were considered to be central to AD risk and pathogenesis, as supported by emerging genetic and transcriptomic studies [ 43 , 44 , 45 , 46 , 47 ]. Increasing evidence demonstrate that microglia emerges as central players in AD. In very early stage, microglia, TREM2 and complement system are responsible for synaptic pruning [ 48 , 49 ]. The processes of activity-dependent and long-term synaptic plasticity are the common and fundamental cellular underpinning of learning and memory which may manifest as influence on long term potential [ 50 ]. Following that, reactive microglia and astrocytes will surround amyloid plaques and secrete numerous pro-inflammatory cytokines. These events are regarded as an early, prime mover in AD evolution. However, non-steroid anti-inflammatory drugs (NSAIDs) did not show enough benefits in clinic. This is because that the relationship between innate immunity and AD pathogenesis is complex, and the immune response can be either deleterious or beneficial depending on the context [ 47 , 51 , 52 ]. However, the new observations that PD-1 immune checkpoint blockade reduces the pathology of AD and improves memory in mouse models of AD [ 53 , 54 , 55 ] give us a direction of future researches.

The recent advances in our understanding of the mechanism underlying microglia dysfunction in pruning, regulating plasticity, and neurogenesis are opening up possibilities for new opportunities of AD therapeutic interventions and diagnosis [ 56 , 57 ]. Targeting these aberrant microglial functions and thereby returning homeostasis may yield novel paradigms for AD therapies. However, given the complexity and diverse functions of microglia in health and disease, there is a crucial need for new biomarkers reflecting the function of specific microglias [ 52 , 58 ].

Cholinergic and oxidative stress hypothesis

Acetylcholine (ACh) is an important neurotransmitter used by cholinergic neurons, which has been involved in critical physiological processes, such as attention, learning, memory, stress response, wakefulness and sleep, and sensory information [ 59 , 60 , 61 , 62 , 63 ]. Cholinergic neurons damage was considered to be a critical pathological change that correlated with cognitive impairment in AD. Thus, cholinergic hypothesis was firstly tested with cholinesterase inhibitors in AD treatment. Tacrine, a cholinesterase inhibitor, was the first anti-AD drug available in clinic [ 64 , 65 , 66 ] although it was withdrawn from the market in 2012 due to severe side effects. Although inhibiting cholinesterase is a symptomatic relief treatment with marginal benefits, it is currently the most available clinical treatment which gives desperate AD patients a glimmer of hope. For other neurotransmitter dysfunction, such as Dopamine and 5-hydroxytryptamine, there are some studies about them, but not much as acetylcholine in AD.

Oxidative stress is considered to play an important role in the pathogenesis of AD. Especially, the brain utilizes more oxygen than other tissues and undergoes mitochondrial respiration, which increases the potential for ROS exposure. In fact, AD is highly associated with cellular oxidative stress, including augmentation of protein oxidation, protein nitration, glycoloxidation and lipid peroxidation as well as accumulation of Aβ, for Aβ can also induce oxidative stress [ 67 , 68 , 69 , 70 , 71 , 72 , 73 ]. Thus, the treatment with anti-oxidant compounds would provide protection against oxidative stress and Aβ toxicity in theory. However, oxidative stress is only a single feature of AD, so antioxidant strategy was challenged for its potency to stop the progression of AD and thus it is proposed as a portion of combination therapy [ 74 , 75 ].

Glucose hypometabolism

Glucose hypometabolism is the early pathogenic event in the prodromal phase of AD, and associated with cognitive and functional decline. Early therapeutic intervention before the irreversible degeneration has become a consensus in AD treatment. Thus, alleviation of glucose hypometabolism was emerged as an attractive strategy of AD treatment. However, most of these therapeutic strategies are targeting mitochondria and bioenergetics, which have shown promise at the preclinical stage but without success in clinical trials [ 76 , 77 ]. Although no strategies are available to alleviate glucose hypometabolism in clinical, glucose metabolism brain imaging such as 18 FDG-PET (Positron emision tomography with 2-deoxy-2-fluorine-18-fluoro-D-glucose) has become a valuable indicator for diagnosis of neurodegenerative diseases that cause dementia, including AD [ 78 ].

Up to now, there’re no effective treatments for changing the course of AD. Confronting these difficulties, we should get deeper understandings about these hypotheses, and meanwhile we should renovate our knowledge about AD and develop new hypothesis.

New pathway to AD

AD is conventionally regarded as a central nervous system (CNS) disorder. However, increasing experimental, epidemiological and clinical evidences have suggested that manifestations of AD extend beyond the brain. Most notably, research over the past few years reveals that the gut microbiome (GMB) has a profound impact on the formation of the blood-brain barrier, myelination, neurogenesis, and microglia maturation [ 79 , 80 , 81 , 82 , 83 , 84 ]. In particular, results from germ-free animals and animals exposed to pathogenic microbial infections, antibiotics, probiotics, or fecal microbiota transplantation showed that gut microbiota modulates many aspects of animal behaviors, suggesting a role for the gut microbiota in host cognition or AD-related pathogenesis [ 85 , 86 , 87 , 88 ]. The underlying mechanisms of gut microbiota influencing brain involve the communication through immune system, the endocrine system, the vague nerve, and the bacteria-derived metabolites.

Immune pathway

The intestinal mucosal lymphoid tissue contains 70% ~ 80% of the immune cells in the whole body, and is considered to be the largest and most important human immune organs. It is also the first line of host defense against pathogens. The human gut contains a large, diverse and dynamic enteric microbiota, including more than 100 trillion microorganisms from at least 1000 distinct species. There’s a complex relationship between intestinal mucosal immune system and intestinal microbiota. Thus, gut microbiota induced immunomodulation is emerging as an important pathway that influences AD [ 89 ].

Gut microbiota can influence brain through immune system in several ways. Firstly, intestinal microbiome can induce cytokines secretion, which enter the circulatory system, pass through blood brain barrier, and directly affect the brain function. For instance, perivascular macrophages and cerebral small vessel epithelial cells can receive the intestinal microbiome produced IL-1 signal and affect central nervous system. Also, gut microbes can activate Toll-like receptors of the brain immune cells (such as microglia) through microbes associated molecular patterns (MAMP). MAMPs can either directly bind to intestinal epithelial cells or infiltrate to the intestine lamina propria to activate lymphocytes, promoting the release of pro-inflammatory cytokines, which further cause subsequent inflammation in brain. Secondly, gut microbes can produce metabolites such as short-chain-fatty acids (SCFAs), gamma-aminobutyric acid (GABA) and 5-HT precursors, which could also travel to the brain via circulatory systems or signal through intestinal epithelials to produce cytokines or neurotransmitters that activate vagus nerve. Thirdly, gut microbes can activate enteroendocrine cells to produce 5-HT, which affect the brain through neuroimmune pathways.

In addition to changing the functions of the immune system, such as through secretion of inflammatory factors or anti-inflammatory factors, intestinal microbiome can also affect the development and composition of immune system. For example, in germ-free mice, isolated lymphoid follicles in gut associated lymphoid tissue are unable to mature, and lymphocytes that are able to secrete IgA in the intestinal epithelium decreased [ 89 , 90 , 91 , 92 ]. For immune system in brain, the deletion of gut microbiota in germ-free mice have global influence on the cell proportions and maturation of microglia in the brain, and thus affect the properties and phenotype of microglia, as compared to conventionally colonized controls [ 93 ]. Similar results were obtained in antibiotic treated mice. Other research also demonstrates that the number of T regulatory cells and T helper lymphocytes (T helper 17, Th17) are significantly reduced in the germ free mouse, indicating the regulatory effects of intestinal microbiome on T cell composition, while microbiome tansplant to germ free mice can modify these variations and restore normal immune function [ 94 , 95 ]. All these modulations of gut microbiota may have direct and indirect effects on AD development and progression.

Endocrine pathway and the vagus nerve

The gut is also the largest endocrine organ in the body. Gut microbiota can regulate secretion of many hormones from intestinal endocrine cells, such as corticosterone and adrenal hormones, and thus establish the information exchange between the intestines and the brain. For example, the intestinal microbiome can affect the secretion of serotonin and regulate brain emotional activities [ 96 , 97 ]; intestinal microbial metabolism can also produce a variety of neurotransmitters, such as dopamine, GABA, acetylcholine and melatonin, which are transmitted to central nervous system through the vagus nerve [ 98 ]. Besides transporting these signal substances, the vagus nerve itself plays an important role in inflammation and depression [ 99 ]. The vagus nerve can influence the gastrointestinal tract, orchestrate the complex interactions between central and peripheral neural control mechanisms [ 100 ]. The stimulation of vagus nerve is able to regulate mood, and the immune system, suggesting the therapeutic potential of vagus nerve modulation to attenuate the pathophysiological changes and restore homeostasis [ 98 , 99 , 100 , 101 , 102 , 103 ].

Bacteria-derived metabolites

Generation of essential nutrients for host physiology, such as vitamins and other cofactors, is an important physiological function of the gut microbiota [ 104 ]. The metabolites of microbiome, such as SCFAs including acetate, butyrate, and propionate, are able to modulate peripheral and central pathologic processes [ 105 ]. For example, butyrate is effective in reducing inflammation and pain. Once in the brain, acetate is able to alter the level of the neurotransmitters glutamate, glutamine, and GABA, as well as increases anorectic neuropeptide expression [ 106 ]. In addition, the gut microbiota can secrete large amounts of amyloids and lipopolysaccharides, which might contribute to the modulation of signaling pathways, the production of proinflammatory cytokines associated with AD pathogenesis and Aβ deposition [ 107 , 108 , 109 ].

In fact, microbiota-gut-brain axis has been established and a disturbed gut microbiota has been incriminated in many neurodegenerative diseases in animal and translational models. In theory, a role for the microbiota-gut-brain axis is highly plausible. However, the theoretical basis for the use of microbiota-directed therapies in neurodegenerative disorders still needs supports from high-quality clinical trials [ 110 ]. To date, only a few studies directly focused on the gut microbiota and AD [ 111 , 112 ], and studies on AD patients is particullarly deficient. A recent research from human showed an increase in the abundance of a pro-inflammatory GMB taxon and a reduction in the abundance of an anti-inflammatory taxon are possibly associated with a peripheral inflammatory state in patients with cognitive impairment and brain amyloidosis. It is important for the research of gut microbiota and AD. However, further investigations are still necessary to explore the possible causal relation between GMB-related inflammation and amyloidosis [ 111 ]. The comprehensive understanding of these underlying mechanisms may provide new insights into these novel therapeutic strategies for AD. In particular, based on the gut microbiota hypothesis, Chinese traditional medicine and probiotic bacteria may play a more important role in therapy [ 113 ].

Conclusions

Nowadays, new technologies are making it possible to get to know enough pathologic details of disease. More importantly, scientists are beginning to treat AD as a systemic disease and they are paying more attention to the correlation between brain and other organs [ 47 , 89 , 114 ]. Perhaps, for complicated disease such as AD, researches and therapies should be based on the principle that combined reductionism with holism, and great efforts should be made to search the fundamental laws of AD by means of multi-scale modeling and efficient numeric assessment. Maybe, just like Chinese traditional medicine [ 115 ], combination treatments or systematic therapy will be a final way out.

Abbreviations

Alzheimer’s disease

Central nervous system

Colony stimulating factor 1 receptor

Gamma-aminobutyric acid

Intravenous immunoglobulin

Microbes associated molecular patterns

Neurofibrillary tangles

N-methyl-D-aspartate

Non-steroid anti-inflammatory drugs

O-linked N-acetylglucosamine

Short-chain-fatty acids

T helper lymphocytes 17

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This review was supported by “Personalized Medicines-Molecular Signature based Drug Discovery and Development”, Strategic Priority Research program of Sciences, Grants No. XDA12040101.

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Du, X., Wang, X. & Geng, M. Alzheimer’s disease hypothesis and related therapies. Transl Neurodegener 7 , 2 (2018). https://doi.org/10.1186/s40035-018-0107-y

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The amyloid hypothesis in Alzheimer disease: new insights from new therapeutics

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Many drugs that target amyloid-β (Aβ) in Alzheimer disease (AD) have failed to demonstrate clinical efficacy. However, four anti-Aβ antibodies have been shown to mediate the removal of amyloid plaque from brains of patients with AD, and the FDA has recently granted accelerated approval to one of these, aducanumab, using reduction of amyloid plaque as a surrogate end point. The rationale for approval and the extent of the clinical benefit from these antibodies are under intense debate. With the aim of informing this debate, we review clinical trial data for drugs that target Aβ from the perspective of the temporal interplay between the two pathognomonic protein aggregates in AD — Aβ plaques and tau neurofibrillary tangles — and their relationship to cognitive impairment, highlighting differences in drug properties that could affect their clinical performance. On this basis, we propose that Aβ pathology drives tau pathology, that amyloid plaque would need to be reduced to a low level (~20 centiloids) to reveal significant clinical benefit and that there will be a lag between the removal of amyloid and the potential to observe a clinical benefit. We conclude that the speed of amyloid removal from the brain by a potential therapy will be important in demonstrating clinical benefit in the context of a clinical trial.

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Karran, E., De Strooper, B. The amyloid hypothesis in Alzheimer disease: new insights from new therapeutics. Nat Rev Drug Discov 21 , 306–318 (2022). https://doi.org/10.1038/s41573-022-00391-w

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8.4: Hypothesis Test Examples for Proportions

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• In a hypothesis test problem, you may see words such as "the level of significance is 1%." The "1%" is the preconceived or preset \(\alpha\).
• The statistician setting up the hypothesis test selects the value of α to use before collecting the sample data.
• If no level of significance is given, a common standard to use is \(\alpha = 0.05\).
• When you calculate the \(p\)-value and draw the picture, the \(p\)-value is the area in the left tail, the right tail, or split evenly between the two tails. For this reason, we call the hypothesis test left, right, or two tailed.
• The alternative hypothesis, \(H_{a}\), tells you if the test is left, right, or two-tailed. It is the key to conducting the appropriate test.
• \(H_{a}\) never has a symbol that contains an equal sign.
• Thinking about the meaning of the \(p\)-value: A data analyst (and anyone else) should have more confidence that he made the correct decision to reject the null hypothesis with a smaller \(p\)-value (for example, 0.001 as opposed to 0.04) even if using the 0.05 level for alpha. Similarly, for a large p -value such as 0.4, as opposed to a \(p\)-value of 0.056 (\(\alpha = 0.05\) is less than either number), a data analyst should have more confidence that she made the correct decision in not rejecting the null hypothesis. This makes the data analyst use judgment rather than mindlessly applying rules.

Full Hypothesis Test Examples

Example \(\PageIndex{7}\)

Joon believes that 50% of first-time brides in the United States are younger than their grooms. She performs a hypothesis test to determine if the percentage is the same or different from 50% . Joon samples 100 first-time brides and 53 reply that they are younger than their grooms. For the hypothesis test, she uses a 1% level of significance.

Set up the hypothesis test:

The 1% level of significance means that α = 0.01. This is a test of a single population proportion .

\(H_{0}: p = 0.50\)  \(H_{a}: p \neq 0.50\)

The words "is the same or different from" tell you this is a two-tailed test.

Calculate the distribution needed:

Random variable: \(P′ =\) the percent of of first-time brides who are younger than their grooms.

Distribution for the test: The problem contains no mention of a mean. The information is given in terms of percentages. Use the distribution for P′ , the estimated proportion.

\[P' - N\left(p, \sqrt{\frac{p-q}{n}}\right)\nonumber \]

\[P' - N\left(0.5, \sqrt{\frac{0.5-0.5}{100}}\right)\nonumber \]

where \(p = 0.50, q = 1−p = 0.50\), and \(n = 100\)

Calculate the p -value using the normal distribution for proportions:

\[p\text{-value} = P(p′ < 0.47 or p′ > 0.53) = 0.5485\nonumber \]

where \[x = 53, p' = \frac{x}{n} = \frac{53}{100} = 0.53\nonumber \].

Interpretation of the \(p\text{-value})\: If the null hypothesis is true, there is 0.5485 probability (54.85%) that the sample (estimated) proportion \(p'\) is 0.53 or more OR 0.47 or less (see the graph in Figure).

\(\mu = p = 0.50\) comes from \(H_{0}\), the null hypothesis.

\(p′ = 0.53\). Since the curve is symmetrical and the test is two-tailed, the \(p′\) for the left tail is equal to \(0.50 – 0.03 = 0.47\) where \(\mu = p = 0.50\). (0.03 is the difference between 0.53 and 0.50.)

Compare \(\alpha\) and the \(p\text{-value}\):

Since \(\alpha = 0.01\) and \(p\text{-value} = 0.5485\). \(\alpha < p\text{-value}\).

Make a decision: Since \(\alpha < p\text{-value}\), you cannot reject \(H_{0}\).

Conclusion: At the 1% level of significance, the sample data do not show sufficient evidence that the percentage of first-time brides who are younger than their grooms is different from 50%.

The \(p\text{-value}\) can easily be calculated.

Press STAT and arrow over to TESTS . Press 5:1-PropZTest . Enter .5 for \(p_{0}\), 53 for \(x\) and 100 for \(n\). Arrow down to Prop and arrow to not equals \(p_{0}\). Press ENTER . Arrow down to Calculate and press ENTER . The calculator calculates the \(p\text{-value}\) (\(p = 0.5485\)) and the test statistic (\(z\)-score). Prop not equals .5 is the alternate hypothesis. Do this set of instructions again except arrow to Draw (instead of Calculate ). Press ENTER . A shaded graph appears with \(\(z\) = 0.6\) (test statistic) and \(p = 0.5485\) (\(p\text{-value}\)). Make sure when you use Draw that no other equations are highlighted in \(Y =\) and the plots are turned off.

The Type I and Type II errors are as follows:

The Type I error is to conclude that the proportion of first-time brides who are younger than their grooms is different from 50% when, in fact, the proportion is actually 50%. (Reject the null hypothesis when the null hypothesis is true).

The Type II error is there is not enough evidence to conclude that the proportion of first time brides who are younger than their grooms differs from 50% when, in fact, the proportion does differ from 50%. (Do not reject the null hypothesis when the null hypothesis is false.)

Exercise \(\PageIndex{7}\)

A teacher believes that 85% of students in the class will want to go on a field trip to the local zoo. She performs a hypothesis test to determine if the percentage is the same or different from 85%. The teacher samples 50 students and 39 reply that they would want to go to the zoo. For the hypothesis test, use a 1% level of significance.

First, determine what type of test this is, set up the hypothesis test, find the \(p\text{-value}\), sketch the graph, and state your conclusion.

Since the problem is about percentages, this is a test of single population proportions.

• \(H_{0} : p = 0.85\)
• \(H_{a}: p \neq 0.85\)
• \(p = 0.7554\)

Because \(p > \alpha\), we fail to reject the null hypothesis. There is not sufficient evidence to suggest that the proportion of students that want to go to the zoo is not 85%.

Example \(\PageIndex{8}\)

Suppose a consumer group suspects that the proportion of households that have three cell phones is 30%. A cell phone company has reason to believe that the proportion is not 30%. Before they start a big advertising campaign, they conduct a hypothesis test. Their marketing people survey 150 households with the result that 43 of the households have three cell phones.

Set up the Hypothesis Test:

\(H_{0}: p = 0.30, H_{a}: p \neq 0.30\)

Determine the distribution needed:

The random variable is \(P′ =\) proportion of households that have three cell phones.

The distribution for the hypothesis test is \(P' - N\left(0.30, \sqrt{\frac{(0.30 \cdot 0.70)}{150}}\right)\)

Exercise 9.6.8.2

a. The value that helps determine the \(p\text{-value}\) is \(p′\). Calculate \(p′\).

a. \(p' = \frac{x}{n}\) where \(x\) is the number of successes and \(n\) is the total number in the sample.

\(x = 43, n = 150\)

\(p′ = 43150\)

Exercise 9.6.8.3

b. What is a success for this problem?

b. A success is having three cell phones in a household.

Exercise 9.6.8.4

c. What is the level of significance?

c. The level of significance is the preset \(\alpha\). Since \(\alpha\) is not given, assume that \(\alpha = 0.05\).

Exercise 9.6.8.5

d. Draw the graph for this problem. Draw the horizontal axis. Label and shade appropriately.

Calculate the \(p\text{-value}\).

d. \(p\text{-value} = 0.7216\)

Exercise 9.6.8.6

e. Make a decision. _____________(Reject/Do not reject) \(H_{0}\) because____________.

e. Assuming that \(\alpha = 0.05, \alpha < p\text{-value}\). The decision is do not reject \(H_{0}\) because there is not sufficient evidence to conclude that the proportion of households that have three cell phones is not 30%.

Exercise \(\PageIndex{8}\)

Marketers believe that 92% of adults in the United States own a cell phone. A cell phone manufacturer believes that number is actually lower. 200 American adults are surveyed, of which, 174 report having cell phones. Use a 5% level of significance. State the null and alternative hypothesis, find the p -value, state your conclusion, and identify the Type I and Type II errors.

• \(H_{0}: p = 0.92\)
• \(H_{a}: p < 0.92\)
• \(p\text{-value} = 0.0046\)

Because \(p < 0.05\), we reject the null hypothesis. There is sufficient evidence to conclude that fewer than 92% of American adults own cell phones.

• Type I Error: To conclude that fewer than 92% of American adults own cell phones when, in fact, 92% of American adults do own cell phones (reject the null hypothesis when the null hypothesis is true).
• Type II Error: To conclude that 92% of American adults own cell phones when, in fact, fewer than 92% of American adults own cell phones (do not reject the null hypothesis when the null hypothesis is false).

The next example is a poem written by a statistics student named Nicole Hart. The solution to the problem follows the poem. Notice that the hypothesis test is for a single population proportion. This means that the null and alternate hypotheses use the parameter \(p\). The distribution for the test is normal. The estimated proportion \(p′\) is the proportion of fleas killed to the total fleas found on Fido. This is sample information. The problem gives a preconceived \(\alpha = 0.01\), for comparison, and a 95% confidence interval computation. The poem is clever and humorous, so please enjoy it!

Example \(\PageIndex{9}\)

My dog has so many fleas,

They do not come off with ease. As for shampoo, I have tried many types Even one called Bubble Hype, Which only killed 25% of the fleas, Unfortunately I was not pleased.

I've used all kinds of soap, Until I had given up hope Until one day I saw An ad that put me in awe.

A shampoo used for dogs Called GOOD ENOUGH to Clean a Hog Guaranteed to kill more fleas.

I gave Fido a bath And after doing the math His number of fleas Started dropping by 3's! Before his shampoo I counted 42.

At the end of his bath, I redid the math And the new shampoo had killed 17 fleas. So now I was pleased.

Now it is time for you to have some fun With the level of significance being .01, You must help me figure out

Use the new shampoo or go without?

\(H_{0}: p \leq 0.25\)   \(H_{a}: p > 0.25\)

In words, CLEARLY state what your random variable \(\bar{X}\) or \(P′\) represents.

\(P′ =\) The proportion of fleas that are killed by the new shampoo

State the distribution to use for the test.

\[N\left(0.25, \sqrt{\frac{(0.25){1-0.25}}{42}}\right)\nonumber \]

Test Statistic: \(z = 2.3163\)

Calculate the \(p\text{-value}\) using the normal distribution for proportions:

\[p\text{-value} = 0.0103\nonumber \]

In one to two complete sentences, explain what the p -value means for this problem.

If the null hypothesis is true (the proportion is 0.25), then there is a 0.0103 probability that the sample (estimated) proportion is 0.4048 \(\left(\frac{17}{42}\right)\) or more.

Use the previous information to sketch a picture of this situation. CLEARLY, label and scale the horizontal axis and shade the region(s) corresponding to the \(p\text{-value}\).

Indicate the correct decision (“reject” or “do not reject” the null hypothesis), the reason for it, and write an appropriate conclusion, using complete sentences.

Conclusion: At the 1% level of significance, the sample data do not show sufficient evidence that the percentage of fleas that are killed by the new shampoo is more than 25%.

Construct a 95% confidence interval for the true mean or proportion. Include a sketch of the graph of the situation. Label the point estimate and the lower and upper bounds of the confidence interval.

Confidence Interval: (0.26,0.55) We are 95% confident that the true population proportion p of fleas that are killed by the new shampoo is between 26% and 55%.

This test result is not very definitive since the \(p\text{-value}\) is very close to alpha. In reality, one would probably do more tests by giving the dog another bath after the fleas have had a chance to return.

Example \(\PageIndex{11}\)

In a study of 420,019 cell phone users, 172 of the subjects developed brain cancer. Test the claim that cell phone users developed brain cancer at a greater rate than that for non-cell phone users (the rate of brain cancer for non-cell phone users is 0.0340%). Since this is a critical issue, use a 0.005 significance level. Explain why the significance level should be so low in terms of a Type I error.

We will follow the four-step process.

• \(H_{0}: p \leq 0.00034\)
• \(H_{a}: p > 0.00034\)

If we commit a Type I error, we are essentially accepting a false claim. Since the claim describes cancer-causing environments, we want to minimize the chances of incorrectly identifying causes of cancer.

• We will be testing a sample proportion with \(x = 172\) and \(n = 420,019\). The sample is sufficiently large because we have \(np = 420,019(0.00034) = 142.8\), \(nq = 420,019(0.99966) = 419,876.2\), two independent outcomes, and a fixed probability of success \(p = 0.00034\). Thus we will be able to generalize our results to the population.

Figure 9.6.11.

Figure 9.6.12.

• Since the \(p\text{-value} = 0.0073\) is greater than our alpha value \(= 0.005\), we cannot reject the null. Therefore, we conclude that there is not enough evidence to support the claim of higher brain cancer rates for the cell phone users.

Example \(\PageIndex{12}\)

According to the US Census there are approximately 268,608,618 residents aged 12 and older. Statistics from the Rape, Abuse, and Incest National Network indicate that, on average, 207,754 rapes occur each year (male and female) for persons aged 12 and older. This translates into a percentage of sexual assaults of 0.078%. In Daviess County, KY, there were reported 11 rapes for a population of 37,937. Conduct an appropriate hypothesis test to determine if there is a statistically significant difference between the local sexual assault percentage and the national sexual assault percentage. Use a significance level of 0.01.

We will follow the four-step plan.

• We need to test whether the proportion of sexual assaults in Daviess County, KY is significantly different from the national average.
• \(H_{0}: p = 0.00078\)
• \(H_{a}: p \neq 0.00078\)

Figure 9.6.13.

Figure 9.6.14.

• Since the \(p\text{-value}\), \(p = 0.00063\), is less than the alpha level of 0.01, the sample data indicates that we should reject the null hypothesis. In conclusion, the sample data support the claim that the proportion of sexual assaults in Daviess County, Kentucky is different from the national average proportion.

The hypothesis test itself has an established process. This can be summarized as follows:

• Determine \(H_{0}\) and \(H_{a}\). Remember, they are contradictory.
• Determine the random variable.
• Determine the distribution for the test.
• Draw a graph, calculate the test statistic, and use the test statistic to calculate the \(p\text{-value}\). (A z -score and a t -score are examples of test statistics.)
• Compare the preconceived α with the p -value, make a decision (reject or do not reject H 0 ), and write a clear conclusion using English sentences.

Notice that in performing the hypothesis test, you use \(\alpha\) and not \(\beta\). \(\beta\) is needed to help determine the sample size of the data that is used in calculating the \(p\text{-value}\). Remember that the quantity \(1 – \beta\) is called the Power of the Test . A high power is desirable. If the power is too low, statisticians typically increase the sample size while keeping α the same.If the power is low, the null hypothesis might not be rejected when it should be.

• Data from Amit Schitai. Director of Instructional Technology and Distance Learning. LBCC.
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• Data from the Bureau of Labor Statistics. Available online at http://www.bls.gov/oes/current/oes291111.htm .
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• Data from Toastmasters International. Available online at http://toastmasters.org/artisan/deta...eID=429&Page=1 .
• Data from Weather Underground. Available online at www.wunderground.com (accessed June 27, 2013).
• Federal Bureau of Investigations. “Uniform Crime Reports and Index of Crime in Daviess in the State of Kentucky enforced by Daviess County from 1985 to 2005.” Available online at http://www.disastercenter.com/kentucky/crime/3868.htm (accessed June 27, 2013).
• “Foothill-De Anza Community College District.” De Anza College, Winter 2006. Available online at research.fhda.edu/factbook/DA...t_da_2006w.pdf.
• Johansen, C., J. Boice, Jr., J. McLaughlin, J. Olsen. “Cellular Telephones and Cancer—a Nationwide Cohort Study in Denmark.” Institute of Cancer Epidemiology and the Danish Cancer Society, 93(3):203-7. Available online at http://www.ncbi.nlm.nih.gov/pubmed/11158188 (accessed June 27, 2013).
• Rape, Abuse & Incest National Network. “How often does sexual assault occur?” RAINN, 2009. Available online at www.rainn.org/get-information...sexual-assault (accessed June 27, 2013).

Contributors and Attributions

Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/[email protected] .