writing an essay on statistics

There are lies, damned lies, and statistics. —Mark Twain

What this handout is about

The purpose of this handout is to help you use statistics to make your argument as effectively as possible.

Introduction

Numbers are power. Apparently freed of all the squishiness and ambiguity of words, numbers and statistics are powerful pieces of evidence that can effectively strengthen any argument. But statistics are not a panacea. As simple and straightforward as these little numbers promise to be, statistics, if not used carefully, can create more problems than they solve.

Many writers lack a firm grasp of the statistics they are using. The average reader does not know how to properly evaluate and interpret the statistics he or she reads. The main reason behind the poor use of statistics is a lack of understanding about what statistics can and cannot do. Many people think that statistics can speak for themselves. But numbers are as ambiguous as words and need just as much explanation.

In many ways, this problem is quite similar to that experienced with direct quotes. Too often, quotes are expected to do all the work and are treated as part of the argument, rather than a piece of evidence requiring interpretation (see our handout on how to quote .) But if you leave the interpretation up to the reader, who knows what sort of off-the-wall interpretations may result? The only way to avoid this danger is to supply the interpretation yourself.

But before we start writing statistics, let’s actually read a few.

Reading statistics

As stated before, numbers are powerful. This is one of the reasons why statistics can be such persuasive pieces of evidence. However, this same power can also make numbers and statistics intimidating. That is, we too often accept them as gospel, without ever questioning their veracity or appropriateness. While this may seem like a positive trait when you plug them into your paper and pray for your reader to submit to their power, remember that before we are writers of statistics, we are readers. And to be effective readers means asking the hard questions. Below you will find a useful set of hard questions to ask of the numbers you find.

1. Does your evidence come from reliable sources?

This is an important question not only with statistics, but with any evidence you use in your papers. As we will see in this handout, there are many ways statistics can be played with and misrepresented in order to produce a desired outcome. Therefore, you want to take your statistics from reliable sources (for more information on finding reliable sources, please see our handout on evaluating print sources ). This is not to say that reliable sources are infallible, but only that they are probably less likely to use deceptive practices. With a credible source, you may not need to worry as much about the questions that follow. Still, remember that reading statistics is a bit like being in the middle of a war: trust no one; suspect everyone.

2. What is the data’s background?

Data and statistics do not just fall from heaven fully formed. They are always the product of research. Therefore, to understand the statistics, you should also know where they come from. For example, if the statistics come from a survey or poll, some questions to ask include:

• Who asked the questions in the survey/poll?
• What, exactly, were the questions?
• Who interpreted the data?
• What issue prompted the survey/poll?
• What (policy/procedure) potentially hinges on the results of the poll?
• Who stands to gain from particular interpretations of the data?

All these questions help you orient yourself toward possible biases or weaknesses in the data you are reading. The goal of this exercise is not to find “pure, objective” data but to make any biases explicit, in order to more accurately interpret the evidence.

3. Are all data reported?

In most cases, the answer to this question is easy: no, they aren’t. Therefore, a better way to think about this issue is to ask whether all data have been presented in context. But it is much more complicated when you consider the bigger issue, which is whether the text or source presents enough evidence for you to draw your own conclusion. A reliable source should not exclude data that contradicts or weakens the information presented.

An example can be found on the evening news. If you think about ice storms, which make life so difficult in the winter, you will certainly remember the newscasters warning people to stay off the roads because they are so treacherous. To verify this point, they tell you that the Highway Patrol has already reported 25 accidents during the day. Their intention is to scare you into staying home with this number. While this number sounds high, some studies have found that the number of accidents actually goes down on days with severe weather. Why is that? One possible explanation is that with fewer people on the road, even with the dangerous conditions, the number of accidents will be less than on an “average” day. The critical lesson here is that even when the general interpretation is “accurate,” the data may not actually be evidence for the particular interpretation. This means you have no way to verify if the interpretation is in fact correct.

There is generally a comparison implied in the use of statistics. How can you make a valid comparison without having all the facts? Good question. You may have to look to another source or sources to find all the data you need.

4. Have the data been interpreted correctly?

If the author gives you her statistics, it is always wise to interpret them yourself. That is, while it is useful to read and understand the author’s interpretation, it is merely that—an interpretation. It is not the final word on the matter. Furthermore, sometimes authors (including you, so be careful) can use perfectly good statistics and come up with perfectly bad interpretations. Here are two common mistakes to watch out for:

• Confusing correlation with causation. Just because two things vary together does not mean that one of them is causing the other. It could be nothing more than a coincidence, or both could be caused by a third factor. Such a relationship is called spurious.The classic example is a study that found that the more firefighters sent to put out a fire, the more damage the fire did. Yikes! I thought firefighters were supposed to make things better, not worse! But before we start shutting down fire stations, it might be useful to entertain alternative explanations. This seemingly contradictory finding can be easily explained by pointing to a third factor that causes both: the size of the fire. The lesson here? Correlation does not equal causation. So it is important not only to think about showing that two variables co-vary, but also about the causal mechanism.
• Ignoring the margin of error. When survey results are reported, they frequently include a margin of error. You might see this written as “a margin of error of plus or minus 5 percentage points.” What does this mean? The simple story is that surveys are normally generated from samples of a larger population, and thus they are never exact. There is always a confidence interval within which the general population is expected to fall. Thus, if I say that the number of UNC students who find it difficult to use statistics in their writing is 60%, plus or minus 4%, that means, assuming the normal confidence interval of 95%, that with 95% certainty we can say that the actual number is between 56% and 64%.

Why does this matter? Because if after introducing this handout to the students of UNC, a new poll finds that only 56%, plus or minus 3%, are having difficulty with statistics, I could go to the Writing Center director and ask for a raise, since I have made a significant contribution to the writing skills of the students on campus. However, she would no doubt point out that a) this may be a spurious relationship (see above) and b) the actual change is not significant because it falls within the margin of error for the original results. The lesson here? Margins of error matter, so you cannot just compare simple percentages.

Finally, you should keep in mind that the source you are actually looking at may not be the original source of your data. That is, if you find an essay that quotes a number of statistics in support of its argument, often the author of the essay is using someone else’s data. Thus, you need to consider not only your source, but the author’s sources as well.

Writing statistics

As you write with statistics, remember your own experience as a reader of statistics. Don’t forget how frustrated you were when you came across unclear statistics and how thankful you were to read well-presented ones. It is a sign of respect to your reader to be as clear and straightforward as you can be with your numbers. Nobody likes to be played for a fool. Thus, even if you think that changing the numbers just a little bit will help your argument, do not give in to the temptation.

As you begin writing, keep the following in mind. First, your reader will want to know the answers to the same questions that we discussed above. Second, you want to present your statistics in a clear, unambiguous manner. Below you will find a list of some common pitfalls in the world of statistics, along with suggestions for avoiding them.

1. The mistake of the “average” writer

Nobody wants to be average. Moreover, nobody wants to just see the word “average” in a piece of writing. Why? Because nobody knows exactly what it means. There are not one, not two, but three different definitions of “average” in statistics, and when you use the word, your reader has only a 33.3% chance of guessing correctly which one you mean.

For the following definitions, please refer to this set of numbers: 5, 5, 5, 8, 12, 14, 21, 33, 38

• Mean (arithmetic mean) This may be the most average definition of average (whatever that means). This is the weighted average—a total of all numbers included divided by the quantity of numbers represented. Thus the mean of the above set of numbers is 5+5+5+8+12+14+21+33+38, all divided by 9, which equals 15.644444444444 (Wow! That is a lot of numbers after the decimal—what do we do about that? Precision is a good thing, but too much of it is over the top; it does not necessarily make your argument any stronger. Consider the reasonable amount of precision based on your input and round accordingly. In this case, 15.6 should do the trick.)
• Median Depending on whether you have an odd or even set of numbers, the median is either a) the number midway through an odd set of numbers or b) a value halfway between the two middle numbers in an even set. For the above set (an odd set of 9 numbers), the median is 12. (5, 5, 5, 8 < 12 < 14, 21, 33, 38)
• Mode The mode is the number or value that occurs most frequently in a series. If, by some cruel twist of fate, two or more values occur with the same frequency, then you take the mean of the values. For our set, the mode would be 5, since it occurs 3 times, whereas all other numbers occur only once.

As you can see, the numbers can vary considerably, as can their significance. Therefore, the writer should always inform the reader which average he or she is using. Otherwise, confusion will inevitably ensue.

2. Match your facts with your questions

Be sure that your statistics actually apply to the point/argument you are making. If we return to our discussion of averages, depending on the question you are interesting in answering, you should use the proper statistics.

Perhaps an example would help illustrate this point. Your professor hands back the midterm. The grades are distributed as follows:

The professor felt that the test must have been too easy, because the average (median) grade was a 95.

When a colleague asked her about how the midterm grades came out, she answered, knowing that her classes were gaining a reputation for being “too easy,” that the average (mean) grade was an 80.

When your parents ask you how you can justify doing so poorly on the midterm, you answer, “Don’t worry about my 63. It is not as bad as it sounds. The average (mode) grade was a 58.”

I will leave it up to you to decide whether these choices are appropriate. Selecting the appropriate facts or statistics will help your argument immensely. Not only will they actually support your point, but they will not undermine the legitimacy of your position. Think about how your parents will react when they learn from the professor that the average (median) grade was 95! The best way to maintain precision is to specify which of the three forms of “average” you are using.

3. Show the entire picture

Sometimes, you may misrepresent your evidence by accident and misunderstanding. Other times, however, misrepresentation may be slightly less innocent. This can be seen most readily in visual aids. Do not shape and “massage” the representation so that it “best supports” your argument. This can be achieved by presenting charts/graphs in numerous different ways. Either the range can be shortened (to cut out data points which do not fit, e.g., starting a time series too late or ending it too soon), or the scale can be manipulated so that small changes look big and vice versa. Furthermore, do not fiddle with the proportions, either vertically or horizontally. The fact that USA Today seems to get away with these techniques does not make them OK for an academic argument.

Charts A, B, and C all use the same data points, but the stories they seem to be telling are quite different. Chart A shows a mild increase, followed by a slow decline. Chart B, on the other hand, reveals a steep jump, with a sharp drop-off immediately following. Conversely, Chart C seems to demonstrate that there was virtually no change over time. These variations are a product of changing the scale of the chart. One way to alleviate this problem is to supplement the chart by using the actual numbers in your text, in the spirit of full disclosure.

Another point of concern can be seen in Charts D and E. Both use the same data as charts A, B, and C for the years 1985-2000, but additional time points, using two hypothetical sets of data, have been added back to 1965. Given the different trends leading up to 1985, consider how the significance of recent events can change. In Chart D, the downward trend from 1990 to 2000 is going against a long-term upward trend, whereas in Chart E, it is merely the continuation of a larger downward trend after a brief upward turn.

One of the difficulties with visual aids is that there is no hard and fast rule about how much to include and what to exclude. Judgment is always involved. In general, be sure to present your visual aids so that your readers can draw their own conclusions from the facts and verify your assertions. If what you have cut out could affect the reader’s interpretation of your data, then you might consider keeping it.

4. Give bases of all percentages

Because percentages are always derived from a specific base, they are meaningless until associated with a base. So even if I tell you that after this reading this handout, you will be 23% more persuasive as a writer, that is not a very meaningful assertion because you have no idea what it is based on—23% more persuasive than what?

Let’s look at crime rates to see how this works. Suppose we have two cities, Springfield and Shelbyville. In Springfield, the murder rate has gone up 75%, while in Shelbyville, the rate has only increased by 10%. Which city is having a bigger murder problem? Well, that’s obvious, right? It has to be Springfield. After all, 75% is bigger than 10%.

Hold on a second, because this is actually much less clear than it looks. In order to really know which city has a worse problem, we have to look at the actual numbers. If I told you that Springfield had 4 murders last year and 7 this year, and Shelbyville had 30 murders last year and 33 murders this year, would you change your answer? Maybe, since 33 murders are significantly more than 7. One would certainly feel safer in Springfield, right?

Not so fast, because we still do not have all the facts. We have to make the comparison between the two based on equivalent standards. To do that, we have to look at the per capita rate (often given in rates per 100,000 people per year). If Springfield has 700 residents while Shelbyville has 3.3 million, then Springfield has a murder rate of 1,000 per 100,000 people, and Shelbyville’s rate is merely 1 per 100,000. Gadzooks! The residents of Springfield are dropping like flies. I think I’ll stick with nice, safe Shelbyville, thank you very much.

Percentages are really no different from any other form of statistics: they gain their meaning only through their context. Consequently, percentages should be presented in context so that readers can draw their own conclusions as you emphasize facts important to your argument. Remember, if your statistics really do support your point, then you should have no fear of revealing the larger context that frames them.

Important questions to ask (and answer) about statistics

• Is the question being asked relevant?
• Do the data come from reliable sources?
• Margin of error/confidence interval—when is a change really a change?
• Are all data reported, or just the best/worst?
• Are the data presented in context?
• Have the data been interpreted correctly?
• Does the author confuse correlation with causation?

Now that you have learned the lessons of statistics, you have two options. Use this knowledge to manipulate your numbers to your advantage, or use this knowledge to better understand and use statistics to make accurate and fair arguments. The choice is yours. Nine out of ten writers, however, prefer the latter, and the other one later regrets his or her decision.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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How To Write a Statistical Analysis Essay

Home » Videos » How To Write a Statistical Analysis Essay

Statistical analysis is a powerful tool used to draw meaningful insights from data. It can be applied to almost any field and has been used in everything from natural sciences, economics, and sociology to sports analytics and business decisions. Writing a statistical analysis essay requires an understanding of the concepts behind it as well as proficiency with data manipulation techniques.

In this guide, we’ll look at the steps involved in writing a statistical analysis essay. Experts in research paper writing from https://domypaper.me/write-my-research-paper/ give detailed instructions on how to properly conduct a statistical analysis and make valid conclusions.

Overview of statistical analysis essays

A statistical analysis essay is an academic paper that involves analyzing quantitative data and interpreting the results. It is often used in social sciences, economics and business to draw meaningful conclusions from the data. The objective of a statistical analysis essay is to analyze a specific dataset or multiple datasets in order to answer a question or prove or disprove a hypothesis. To achieve this effectively, the information must be analyzed using appropriate statistical techniques such as descriptive statistics, inferential statistics, regression analysis and correlation analysis.

Researching the subject matter

Before writing your statistical analysis essay it is important to research the subject matter thoroughly so that you have an understanding of what you are dealing with. This can include collecting and organizing any relevant data sets as well as researching different types of statistical techniques available for analyzing them. Furthermore, it is important to become familiar with the terminology associated with statistical analysis such as mean, median and mode.

Structuring your statistical analysis essay

The structure of your essay will depend on the type of data you are analyzing and the research question or hypothesis that you are attempting to answer. Generally speaking, it should include an introduction which introduces the research question or hypothesis; a body section which includes an overview of relevant literature; a description of how the data was collected and analyzed and any conclusions drawn from it; and finally a conclusion which summarizes all findings.

Analyzing data and drawing conclusions from it

After collecting and organizing your data, you must analyze it in order to draw meaningful conclusions from it. This involves using appropriate statistical techniques such as descriptive statistics, inferential statistics, regression analysis and correlation analysis. It is important to understand the assumptions made when using each technique in order to analyze the data correctly and draw accurate conclusions from it. When choosing a statistical technique for your research, it is important to consult with an expert https://typemyessay.me/service/research-paper-writing-service who can guide you on the most appropriate approach for your study.

Interpreting results and writing a conclusion

Once you have analyzed the data successfully, you must interpret the results carefully in order to answer your research question or prove/disprove your hypothesis. This involves making sure that any conclusions drawn are soundly based on the evidence presented. After interpreting the results, you should write a conclusion which summarizes all of your findings.

Using sources in your analysis

In order to back up your claims and provide support for your arguments, it is important to use credible sources within your analysis. This could include peer-reviewed articles, journals and books which can provide evidence to support your conclusion. It is also important to cite all sources used in order to avoid plagiarism.

Proofreading and finalizing your work

Once you have written your essay it is important to proofread it carefully before submitting it. This involves checking for grammar, spelling and punctuation errors as well as ensuring that the flow of the essay makes sense. Additionally, make sure that any references cited are correct and up-to-date. If you find it hard to complete your research statistical paper, you may want to consider buying a research paper for sale . This service can save you time and money, allowing you to focus on other important tasks.

Tips for writing a successful statistical analysis essay

Here are some tips for writing a successful statistical analysis essay:

• Research your subject matter thoroughly before writing your essay.
• Structure your paper according to the type of data you are analyzing.
• Analyze your data using appropriate statistical techniques.
• Interpret and draw meaningful conclusions from your results.
• Use credible sources to back up any claims or arguments made.
• Proofread and finalize your work before submitting it.

These tips will help ensure that your essay is well researched, structured correctly and contains accurate information. Following these tips will help you write a successful statistical analysis essay which can be used to answer research questions or prove/disprove hypotheses.

Sources and links For the articles and videos I use different databases, such as Eurostat, OECD World Bank Open Data, Data Gov and others. You are free to use the video I have made on your site using the link or the embed code. If you have any questions, don’t hesitate to write to me!

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Introductory essay

Written by the educators who created Visualizing Data, a brief look at the key facts, tough questions and big ideas in their field. Begin this TED Study with a fascinating read that gives context and clarity to the material.

The reality of today

All of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information...And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. David McCandless

In today's complex 'information jungle,' David McCandless observes that "Data is the new soil." McCandless, a data journalist and information designer, celebrates data as a ubiquitous resource providing a fertile and creative medium from which new ideas and understanding can grow. McCandless's inspiration, statistician Hans Rosling, builds on this idea in his own TEDTalk with his compelling image of flowers growing out of data/soil. These 'flowers' represent the many insights that can be gleaned from effective visualization of data.

We're just learning how to till this soil and make sense of the mountains of data constantly being generated. As Gary King, Director of Harvard's Institute for Quantitative Social Science says in his New York Times article "The Age of Big Data":

It's a revolution. We're really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.

How do we deal with all this data without getting information overload? How do we use data to gain real insight into the world? Finding ways to pull interesting information out of data can be very rewarding, both personally and professionally. The managing editor of Financial Times observed on CNN's Your Money : "The people who are able to in a sophisticated and practical way analyze that data are going to have terrific jobs." Those who learn how to present data in effective ways will be valuable in every field.

Many people, when they think of data, think of tables filled with numbers. But this long-held notion is eroding. Today, we're generating streams of data that are often too complex to be presented in a simple "table." In his TEDTalk, Blaise Aguera y Arcas explores images as data, while Deb Roy uses audio, video, and the text messages in social media as data.

Some may also think that only a few specialized professionals can draw insights from data. When we look at data in the right way, however, the results can be fun, insightful, even whimsical — and accessible to everyone! Who knew, for example, that there are more relationship break-ups on Monday than on any other day of the week, or that the most break-ups (at least those discussed on Facebook) occur in mid-December? David McCandless discovered this by analyzing thousands of Facebook status updates.

Data, data, everywhere

There is more data available to us now than we can possibly process. Every minute , Internet users add the following to the big data pool (i):

• 204,166,667 email messages sent
• More than 2,000,000 Google searches
• 684,478 pieces of content added on Facebook
• $272,070 spent by consumers via online shopping
• More than 100,000 tweets on Twitter
• 47,000 app downloads from Apple
• 34,722 "likes" on Facebook for different brands and organizations
• 27,778 new posts on Tumblr blogs
• 3,600 new photos on Instagram
• 3,125 new photos on Flickr
• 2,083 check-ins on Foursquare
• 571 new websites created
• 347 new blog posts published on Wordpress
• 217 new mobile web users
• 48 hours of new video on YouTube

These numbers are almost certainly higher now, as you read this. And this just describes a small piece of the data being generated and stored by humanity. We're all leaving data trails — not just on the Internet, but in everything we do. This includes reams of financial data (from credit cards, businesses, and Wall Street), demographic data on the world's populations, meteorological data on weather and the environment, retail sales data that records everything we buy, nutritional data on food and restaurants, sports data of all types, and so on.

Governments are using data to search for terrorist plots, retailers are using it to maximize marketing strategies, and health organizations are using it to track outbreaks of the flu. But did you ever think of collecting data on every minute of your child's life? That's precisely what Deb Roy did. He recorded 90,000 hours of video and 140,000 hours of audio during his son's first years. That's a lot of data! He and his colleagues are using the data to understand how children learn language, and they're now extending this work to analyze publicly available conversations on social media, allowing them to take "the real-time pulse of a nation."

Data can provide us with new and deeper insight into our world. It can help break stereotypes and build understanding. But the sheer quantity of data, even in just any one small area of interest, is overwhelming. How can we make sense of some of this data in an insightful way?

The power of visualizing data

Visualization can help transform these mountains of data into meaningful information. In his TEDTalk, David McCandless comments that the sense of sight has by far the fastest and biggest bandwidth of any of the five senses. Indeed, about 80% of the information we take in is by eye. Data that seems impenetrable can come alive if presented well in a picture, graph, or even a movie. Hans Rosling tells us that "Students get very excited — and policy-makers and the corporate sector — when they can see the data."

It makes sense that, if we can effectively display data visually, we can make it accessible and understandable to more people. Should we worry, however, that by condensing data into a graph, we are simplifying too much and losing some of the important features of the data? Let's look at a fascinating study conducted by researchers Emre Soyer and Robin Hogarth . The study was conducted on economists, who are certainly no strangers to statistical analysis. Three groups of economists were asked the same question concerning a dataset:

• One group was given the data and a standard statistical analysis of the data; 72% of these economists got the answer wrong.
• Another group was given the data, the statistical analysis, and a graph; still 61% of these economists got the answer wrong.
• A third group was given only the graph, and only 3% got the answer wrong.

Visualizing data can sometimes be less misleading than using the raw numbers and statistics!

What about all the rest of us, who may not be professional economists or statisticians? Nathalie Miebach finds that making art out of data allows people an alternative entry into science. She transforms mountains of weather data into tactile physical structures and musical scores, adding both touch and hearing to the sense of sight to build even greater understanding of data.

Another artist, Chris Jordan, is concerned about our ability to comprehend big numbers. As citizens of an ever-more connected global world, we have an increased need to get useable information from big data — big in terms of the volume of numbers as well as their size. Jordan's art is designed to help us process such numbers, especially numbers that relate to issues of addiction and waste. For example, Jordan notes that the United States has the largest percentage of its population in prison of any country on earth: 2.3 million people in prison in the United States in 2005 and the number continues to rise. Jordan uses art, in this case a super-sized image of 2.3 million prison jumpsuits, to help us see that number and to help us begin to process the societal implications of that single data value. Because our brains can't truly process such a large number, his artwork makes it real.

The role of technology in visualizing data

The TEDTalks in this collection depend to varying degrees on sophisticated technology to gather, store, process, and display data. Handling massive amounts of data (e.g., David McCandless tracking 10,000 changes in Facebook status, Blaise Aguera y Arcas synching thousands of online images of the Notre Dame Cathedral, or Deb Roy searching for individual words in 90,000 hours of video tape) requires cutting-edge computing tools that have been developed specifically to address the challenges of big data. The ability to manipulate color, size, location, motion, and sound to discover and display important features of data in a way that makes it readily accessible to ordinary humans is a challenging task that depends heavily on increasingly sophisticated technology.

The importance of good visualization

There are good ways and bad ways of presenting data. Many examples of outstanding presentations of data are shown in the TEDTalks. However, sometimes visualizations of data can be ineffective or downright misleading. For example, an inappropriate scale might make a relatively small difference look much more substantial than it should be, or an overly complicated display might obfuscate the main relationships in the data. Statistician Kaiser Fung's blog Junk Charts offers many examples of poor representations of data (and some good ones) with descriptions to help the reader understand what makes a graph effective or ineffective. For more examples of both good and bad representations of data, see data visualization architect Andy Kirk's blog at visualisingdata.com . Both consistently have very current examples from up-to-date sources and events.

Creativity, even artistic ability, helps us see data in new ways. Magic happens when interesting data meets effective design: when statistician meets designer (sometimes within the same person). We are fortunate to live in a time when interactive and animated graphs are becoming commonplace, and these tools can be incredibly powerful. Other times, simpler graphs might be more effective. The key is to present data in a way that is visually appealing while allowing the data to speak for itself.

Changing perceptions through data

While graphs and charts can lead to misunderstandings, there is ultimately "truth in numbers." As Steven Levitt and Stephen Dubner say in Freakonomics , "[T]eachers and criminals and real-estate agents may lie, and politicians, and even C.I.A. analysts. But numbers don't." Indeed, consideration of data can often be the easiest way to glean objective insights. Again from Freakonomics : "There is nothing like the sheer power of numbers to scrub away layers of confusion and contradiction."

Data can help us understand the world as it is, not as we believe it to be. As Hans Rosling demonstrates, it's often not ignorance but our preconceived ideas that get in the way of understanding the world as it is. Publicly-available statistics can reshape our world view: Rosling encourages us to "let the dataset change your mindset."

Chris Jordan's powerful images of waste and addiction make us face, rather than deny, the facts. It's easy to hear and then ignore that we use and discard 1 million plastic cups every 6 hours on airline flights alone. When we're confronted with his powerful image, we engage with that fact on an entirely different level (and may never see airline plastic cups in the same way again).

The ability to see data expands our perceptions of the world in ways that we're just beginning to understand. Computer simulations allow us to see how diseases spread, how forest fires might be contained, how terror networks communicate. We gain understanding of these things in ways that were unimaginable only a few decades ago. When Blaise Aguera y Arcas demonstrates Photosynth, we feel as if we're looking at the future. By linking together user-contributed digital images culled from all over the Internet, he creates navigable "immensely rich virtual models of every interesting part of the earth" created from the collective memory of all of us. Deb Roy does somewhat the same thing with language, pulling in publicly available social media feeds to analyze national and global conversation trends.

Roy sums it up with these powerful words: "What's emerging is an ability to see new social structures and dynamics that have previously not been seen. ...The implications here are profound, whether it's for science, for commerce, for government, or perhaps most of all, for us as individuals."

Let's begin with the TEDTalk from David McCandless, a self-described "data detective" who describes how to highlight hidden patterns in data through its artful representation.

David McCandless

The beauty of data visualization.

i. Data obtained June 2012 from “How Much Data Is Created Every Minute?” on http://mashable.com/2012/06/22/data-created-every-minute/ .

Relevant talks

Hans Rosling

The magic washing machine.

Nathalie Miebach

Art made of storms.

Chris Jordan

Turning powerful stats into art.

Blaise Agüera y Arcas

How photosynth can connect the world's images.

The birth of a word

Statistics - List of Free Essay Examples And Topic Ideas

Statistics, as the science of collecting, analyzing, and interpreting data, plays an indispensable role in modern decision-making and knowledge generation. Essays could explore the myriad applications of statistics across various fields including healthcare, economics, and social sciences. They might delve into key statistical concepts, methods, and tools, illustrating how they help in understanding complex phenomena, making predictions, and informing policy. Discussions might also extend to the ethical considerations inherent in statistical practices, such as data integrity, privacy, and the potential for misrepresentation or bias. The discourse may also touch on the evolving landscape of statistics amid the advent of big data and computational advancements, examining how these developments are expanding the capabilities and applications of statistical analysis. We have collected a large number of free essay examples about Statistics you can find at PapersOwl Website. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

Gender Wage Inequaity in the United States: Statistics and Solutions

"There is a deeply ingrained ideology amongst people in our society that men are the movers and shakers in the business world. This refers to the point of view that men are limited to working in major companies and businesses, and women are limited to the domestic domain. This may have been a true reflection of life fifty years ago, but today a new trend is developing in American society. The levels of education amongst women are increasing, which leads […]

Same-Sex Marriage – Statistics

Marriage was determined to be a fundamental right in Baskin and Obergefell. With many fundamental rights, the right should be considered reversible. Individuals can defer their fundamental rights such as the rights to bear arms, speech, and religion. Therefore, deciding not to marry should also be seen as fundamental. Society has always had strong views on marriage. “Most people think it’s important for couples who intend to stay together to be married, but the number of single Americans who want […]

Hazard of Climate Changing

Sustainability is more than just a term, it's the logic of earth and methods/technique a businesses/people must follow to achieve goals that won't harm the environment in the meanwhile still good socially and increasing the economy. In my paper, I would like to discuss how could the climate change be harmful to sustainability and how it may have an affect on all aspects of the sustainability. According to Reed Karaim in his article about Climate change, he claims that climate […]

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Statistics on Adolescent Suicide

What are your fondest memories playing as a young child? Some of us will remember chasing after a soccer ball or throwing a football across the yard. Others may remember jumping up and down erupting with glee while pretending to be a cheerleader or hitting a baseball across the neighbor’s fence with an aluminum bat. However, a few might not remember playing outside or participating in any sports at all because their parents were engulfed with fear of them getting […]

The Effect of Coffee Consumption on the Risk of Hypertension

ABSTRACT BACKGROUND: hypertension can be defined as a disorder that makes the blood to exert some forces against the walls of the blood vessels. This force depends on the rate of heart beats as well as the resistance from the blood vessels. The medical guidelines define this disorder as pressure higher than 140 over 90 millimeters of mercury (mmHg). AIM: Caffeine compounds are present in coffee and tea. We aimed to evaluate the impact of chronic coffee or tea consumption […]

Inferential Stats Analysis for Psychology

Concerning the data collected, it means that it is easier to draw a valid conclusion regarding the manner in which their variable relates to each group. In this way, it was easier to determine or provide the means of testing the validity of the outcome as well as inferring their characteristics just from a small sample of the participants into a larger one (Goodwin & Goodwin, 2017). In so doing, it implies that it was easier to tell how the […]

Discuss the Importance of Data Management in Research

1. Definiton of Key terms Data management is a general term which refers to a part of research process involving organising, structuring, storage and care of data generated during the research process. It is of prime importance in that it is part of good research practice and it has a bearing on the quality of analysis and research output. The University of Edinburgh (2014) defines data management as a general term covering how you organize, structure, store and care for […]

The Relationship between Early Pregnancy and Wages

Abstract The purpose of this research is to investigate the existence of a possible relationship between early pregnancy and wages. Findings within my research may provide policymakers with critical information required to make decisions that may avert premature pregnancy. Furthermore, I hope the findings of my investigation can help motivate policymakers to focus their efforts on groups that are harmed more due to early pregnancy. The regression analyzes cross-sectional data from 2017 which includes all fifty states. Within the study, […]

College and African American Male: Basketball Athletes

As a freshman in college, I acknowledge and recognize the fact that college can be a challenging experience. The college experience can become even more challenging when you factor in sororities, clubs, fraternities, sports and other school activities. The article that I have decided to use for my analysis is, “College and the African American Male Athlete by Stephen Brown.” Stephen Brown’s main source comes from the book Closing the Education Achievement Gaps for African American Males by Theodore S. […]

Racial Stereotypes in Athletics

The article, Racial Athletic Stereotype Confirmation in College Football Recruiting, can be found in the Journal of Social Psychology and is written by Grant Thomas, Jessica J. Good, and Alexi R. Gross. This article was published in 2015 and it explores the topic of racial stereotypes in the context of college athletic recruitment. They were basically studying if a racial bias could play a role in college athletic recruitment. The researchers' first hypothesis was that coaches would rate black players […]

UNIVERSITY of SOUTH AUSTRALIA 

Introduction In quantitative methods a systematic empirical observation through statistical, mathematical and computational techniques are important components. Reliability of the data is important in quantitative methods. Data accuracy is affected by a variety of factors which range from the choice of the collection methods to biasness. Data is important in improving several aspects of business it is therefore imperative for any business to carry out quantitative research. The data provided in the appendices can is helpful in determining the relationships […]

Customer Success/Customer Engagement

Introduction Customer success and customer engagement are important concepts in every company or business-oriented organization. There are various concerns about the concepts of customer engagement and customer success, as well as their importance for various companies. However, studies have also taken a keen interest in various issues associated with customer engagement through different strategies. From this description, it is clear that customer engagement is a critical concern for every management team with regards to fulfilling the needs of the customers […]

Psychological Survey Study

Questions and Answers 1. How are families likely to view your age/gender/race/ethnicity/spirituality etc. and what cultural blind spots or considerations do you need to take into account when you start working with a family (or about a family that you know)?Families tend to view a person?'s ideas based on their age. In most cases young persons' ideas may be discriminated simply because they are young  therefore, family members tend to think that the younger you are, the less informed you […]

Racism: Unmasking Microaggressions and Discrimination

Reading through the article provided a vivid reflection on how racism becomes a serious issue in the today society. There are various types of racism the article brings out manifested in micro aggression form. The varied opinions in my mind provide a clear picture of the information relayed in the article through the following analysis. Discrimination concerning race will major in my analysis. First, let me talk about the black guy abused in the Saudi Arabia that has sparked public […]

Memphis Crime Rate: a Closer Look at the Statistics

In the annals of cultural heritage and musical genesis, Memphis stands as an emblem of profound resonance, heralded as the cradle of blues melody. Yet, amidst its illustrious tapestry, the city grapples with the stark limelight of crime statistics. A scrutiny of Memphis's crime metrics unveils a labyrinthine narrative, necessitating a discerning comprehension of the socio-economic and cultural dynamics at play. The city's crime landscape, particularly in the realm of violent transgressions, often eclipses the national benchmark, eliciting both trepidation […]

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How to write a statistics essay: short guide.

Statistics is an incredibly useful subject, particularly in today's data-driven world, and it frequently goes hand in hand with tools. For example excel is renowned for its ability to handle a variety of complex calculations, making it an indispensable tool for students tackling statistical problems. However, mastering requires a solid foundation of knowledge, which some students may lack. This is where the integration of STEM-focused Excel courses in many universities becomes beneficial, providing students with the necessary skills to utilize effectively for statistical analysis. Nevertheless, when students encounter difficulties, PapersOwl presents a solution with excel help online.

Their experts are adept in both statistics, offering personalized assistance to students who struggle with using Excel for their statistical assignments.

Writing a statistics essay involves more than just presenting numbers and data. It requires a clear understanding of statistical methods, an ability to interpret results, and the skill to communicate findings effectively. This article provides a step-by-step guide on how to write a compelling statistics essay.

Understanding the Essay Question

Firstly, it's essential to comprehend the specific question or topic you are dealing with. A statistics essay could range from analyzing a set of data to discussing a particular statistical method. Understanding the scope, requirements, and objectives of the essay will guide your research and writing process.

Research and Data Collection

Begin by collecting relevant data for your essay. This could involve gathering existing data or conducting your own research. Ensure that your sources are credible and that your data is accurate. Additionally, familiarize yourself with the statistical methods that are appropriate for analyzing your data.

Planning Your Essay

Organize your thoughts and data before you start writing. This includes outlining the structure of your essay and deciding how you will present your data. A typical structure might include an introduction, a methodology section, a data analysis section, and a conclusion.

Writing the Introduction

Your introduction should set the context for your essay. Explain why the topic is important and how your essay addresses it. Introduce your thesis statement or the main argument of your essay.

Methodology

In this section, describe the methods used to collect and analyze your data. Be detailed so that readers understand how you arrived at your conclusions. This might include discussing sample sizes, variables, and statistical tests used.

Data Analysis

This is the core of your statistics essay. Present your data in a clear and structured manner. Use graphs, tables, and charts to illustrate your points. Interpret the results of your analysis, explaining what the data shows and why it is significant.

Discussing Results

Go beyond just presenting data. Discuss what the results mean in the context of your topic. Compare your findings with other studies and theories. Address any limitations in your study and suggest areas for further research.

Summarize the main points of your essay, restating your thesis in light of the evidence presented. Highlight the significance of your findings and how they contribute to the understanding of the topic.

Referencing and Citation

Accurately cite all the sources and data used in your essay. Follow the required citation style (APA, MLA, Chicago, etc.). Proper citation is essential to avoid plagiarism and to give credit to the original authors.

Proofreading and Editing

Finally, revise your essay. Check for grammatical and spelling errors, ensure clarity and flow, and verify that all data is accurately presented. Peer reviews can be helpful in identifying areas for improvement.

In conclusion, writing a statistics essay requires careful planning, thorough research, and clear presentation of data and findings. By following these guidelines, you can effectively communicate complex statistical information and insights, contributing meaningfully to the topic of discussion.

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The Beginner's Guide to Writing an Essay | Steps & Examples

An academic essay is a focused piece of writing that develops an idea or argument using evidence, analysis, and interpretation.

There are many types of essays you might write as a student. The content and length of an essay depends on your level, subject of study, and course requirements. However, most essays at university level are argumentative — they aim to persuade the reader of a particular position or perspective on a topic.

The essay writing process consists of three main stages:

• Preparation: Decide on your topic, do your research, and create an essay outline.
• Writing : Set out your argument in the introduction, develop it with evidence in the main body, and wrap it up with a conclusion.
• Revision:  Check your essay on the content, organization, grammar, spelling, and formatting of your essay.

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Table of contents

Essay writing process, preparation for writing an essay, writing the introduction, writing the main body, writing the conclusion, essay checklist, lecture slides, frequently asked questions about writing an essay.

The writing process of preparation, writing, and revisions applies to every essay or paper, but the time and effort spent on each stage depends on the type of essay .

For example, if you’ve been assigned a five-paragraph expository essay for a high school class, you’ll probably spend the most time on the writing stage; for a college-level argumentative essay , on the other hand, you’ll need to spend more time researching your topic and developing an original argument before you start writing.

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Before you start writing, you should make sure you have a clear idea of what you want to say and how you’re going to say it. There are a few key steps you can follow to make sure you’re prepared:

• Understand your assignment: What is the goal of this essay? What is the length and deadline of the assignment? Is there anything you need to clarify with your teacher or professor?
• Define a topic: If you’re allowed to choose your own topic , try to pick something that you already know a bit about and that will hold your interest.
• Do your research: Read  primary and secondary sources and take notes to help you work out your position and angle on the topic. You’ll use these as evidence for your points.
• Come up with a thesis:  The thesis is the central point or argument that you want to make. A clear thesis is essential for a focused essay—you should keep referring back to it as you write.
• Create an outline: Map out the rough structure of your essay in an outline . This makes it easier to start writing and keeps you on track as you go.

Once you’ve got a clear idea of what you want to discuss, in what order, and what evidence you’ll use, you’re ready to start writing.

The introduction sets the tone for your essay. It should grab the reader’s interest and inform them of what to expect. The introduction generally comprises 10–20% of the text.

1. Hook your reader

The first sentence of the introduction should pique your reader’s interest and curiosity. This sentence is sometimes called the hook. It might be an intriguing question, a surprising fact, or a bold statement emphasizing the relevance of the topic.

Let’s say we’re writing an essay about the development of Braille (the raised-dot reading and writing system used by visually impaired people). Our hook can make a strong statement about the topic:

The invention of Braille was a major turning point in the history of disability.

2. Provide background on your topic

Next, it’s important to give context that will help your reader understand your argument. This might involve providing background information, giving an overview of important academic work or debates on the topic, and explaining difficult terms. Don’t provide too much detail in the introduction—you can elaborate in the body of your essay.

3. Present the thesis statement

Next, you should formulate your thesis statement— the central argument you’re going to make. The thesis statement provides focus and signals your position on the topic. It is usually one or two sentences long. The thesis statement for our essay on Braille could look like this:

As the first writing system designed for blind people’s needs, Braille was a groundbreaking new accessibility tool. It not only provided practical benefits, but also helped change the cultural status of blindness.

4. Map the structure

In longer essays, you can end the introduction by briefly describing what will be covered in each part of the essay. This guides the reader through your structure and gives a preview of how your argument will develop.

The invention of Braille marked a major turning point in the history of disability. The writing system of raised dots used by blind and visually impaired people was developed by Louis Braille in nineteenth-century France. In a society that did not value disabled people in general, blindness was particularly stigmatized, and lack of access to reading and writing was a significant barrier to social participation. The idea of tactile reading was not entirely new, but existing methods based on sighted systems were difficult to learn and use. As the first writing system designed for blind people’s needs, Braille was a groundbreaking new accessibility tool. It not only provided practical benefits, but also helped change the cultural status of blindness. This essay begins by discussing the situation of blind people in nineteenth-century Europe. It then describes the invention of Braille and the gradual process of its acceptance within blind education. Subsequently, it explores the wide-ranging effects of this invention on blind people’s social and cultural lives.

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The body of your essay is where you make arguments supporting your thesis, provide evidence, and develop your ideas. Its purpose is to present, interpret, and analyze the information and sources you have gathered to support your argument.

Length of the body text

The length of the body depends on the type of essay. On average, the body comprises 60–80% of your essay. For a high school essay, this could be just three paragraphs, but for a graduate school essay of 6,000 words, the body could take up 8–10 pages.

Paragraph structure

To give your essay a clear structure , it is important to organize it into paragraphs . Each paragraph should be centered around one main point or idea.

That idea is introduced in a  topic sentence . The topic sentence should generally lead on from the previous paragraph and introduce the point to be made in this paragraph. Transition words can be used to create clear connections between sentences.

After the topic sentence, present evidence such as data, examples, or quotes from relevant sources. Be sure to interpret and explain the evidence, and show how it helps develop your overall argument.

Lack of access to reading and writing put blind people at a serious disadvantage in nineteenth-century society. Text was one of the primary methods through which people engaged with culture, communicated with others, and accessed information; without a well-developed reading system that did not rely on sight, blind people were excluded from social participation (Weygand, 2009). While disabled people in general suffered from discrimination, blindness was widely viewed as the worst disability, and it was commonly believed that blind people were incapable of pursuing a profession or improving themselves through culture (Weygand, 2009). This demonstrates the importance of reading and writing to social status at the time: without access to text, it was considered impossible to fully participate in society. Blind people were excluded from the sighted world, but also entirely dependent on sighted people for information and education.

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The conclusion is the final paragraph of an essay. It should generally take up no more than 10–15% of the text . A strong essay conclusion :

• Returns to your thesis
• Ties together your main points
• Shows why your argument matters

A great conclusion should finish with a memorable or impactful sentence that leaves the reader with a strong final impression.

What not to include in a conclusion

To make your essay’s conclusion as strong as possible, there are a few things you should avoid. The most common mistakes are:

• Including new arguments or evidence
• Undermining your arguments (e.g. “This is just one approach of many”)
• Using concluding phrases like “To sum up…” or “In conclusion…”

Braille paved the way for dramatic cultural changes in the way blind people were treated and the opportunities available to them. Louis Braille’s innovation was to reimagine existing reading systems from a blind perspective, and the success of this invention required sighted teachers to adapt to their students’ reality instead of the other way around. In this sense, Braille helped drive broader social changes in the status of blindness. New accessibility tools provide practical advantages to those who need them, but they can also change the perspectives and attitudes of those who do not.

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Checklist: Essay

My essay follows the requirements of the assignment (topic and length ).

My introduction sparks the reader’s interest and provides any necessary background information on the topic.

My introduction contains a thesis statement that states the focus and position of the essay.

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An essay is a focused piece of writing that explains, argues, describes, or narrates.

In high school, you may have to write many different types of essays to develop your writing skills.

Academic essays at college level are usually argumentative : you develop a clear thesis about your topic and make a case for your position using evidence, analysis and interpretation.

The structure of an essay is divided into an introduction that presents your topic and thesis statement , a body containing your in-depth analysis and arguments, and a conclusion wrapping up your ideas.

The structure of the body is flexible, but you should always spend some time thinking about how you can organize your essay to best serve your ideas.

Your essay introduction should include three main things, in this order:

• An opening hook to catch the reader’s attention.
• Relevant background information that the reader needs to know.
• A thesis statement that presents your main point or argument.

The length of each part depends on the length and complexity of your essay .

A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

A topic sentence is a sentence that expresses the main point of a paragraph . Everything else in the paragraph should relate to the topic sentence.

At college level, you must properly cite your sources in all essays , research papers , and other academic texts (except exams and in-class exercises).

Add a citation whenever you quote , paraphrase , or summarize information or ideas from a source. You should also give full source details in a bibliography or reference list at the end of your text.

The exact format of your citations depends on which citation style you are instructed to use. The most common styles are APA , MLA , and Chicago .

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Importance of Statistics in Daily Life Essay

In order to explore the use of statistics in everyday life, the essay should start with a debatable statement. On a daily basis, people collect and analyze a lot of information presented in numbers, and it is closely associated with different aspects of their lives. Thus, it is typical to apply the elementary statistical approaches to examining the learned material about everyday activities. It can help to get the average results in relation to actual events or phenomena.

However, many people do not guess that they use such principles as the base for their knowledge. So, to reveal the importance of statistics in daily life, this essay will provide different real-life examples and explain the application of various data analysis methods.

Moreover, when persons have to present the solution to this or that question or decide how to act in the definite situation they also use the statistical data on the issue as one of the main arguments which can influence the further development of the case. That is why statistics can be defined as the science which deals with the data’s collection and its interpretation according to the certain task, and the results of the research can be effectively used in many spheres. From this point, the relative value of statistics for the everyday life is in the fact that people have the opportunity to plan their actions according to the statistical data with references to those results which can satisfy or not their expectations.

People are usually interested in the average temperature and the weather forecasts, in the amount of people who prefer this or that product which they usually purchase. These persons listen to the economical news in which the data of statistics on the state’s development are presented and pay attention to the risks of the transport incidents before going out the house. The statistical data influence all the aspects of the people’s life during the whole day.

When an individual wants to learn about the latest news he concentrates on the information which is interesting for him personally, and these facts are often given in the form of numbers. The average results in different fields and areas from the average level of incomes in the country and the average level of attendance the local library till the average data on the consumers’ preference of brands and services can provide the basics for the people’s choices and usual decisions which are made as a part of the daily rituals and routines.

One more effective advantage of statistics is the possibility to offer the prognoses of the development of definite situations and processes. People are inclined to use the statistical prognoses when they plan such significant changes in their life as the search of the new job, new investments in companies, travelling, and long-term projects. Statistics as the science is based on the strict mathematical calculations and formulas (Bluman, 2009). That is why its methods can be discussed as the effective ways of interpreting the collected quantitative information on any aspect of the life.

It is possible to analyze the tendencies of the world’s development with references to the statistical approach and use this approach as the means to organize the everyday life according to these trends. Furthermore, many people focus on the results of the statistical researches not only at the elementary level in their daily life but also as the part of their work. Thus, accounting, economics, logistics, and many other spheres of the knowledge use statistics (Black, 2009). Moreover, working with their computers, people often refer to the statistical analysis of the data in order to receive the average result or form the picture of the process’s development (Mann, 2010).

Today, it is not necessary for people to examine and test a lot of material to get the information about its appropriateness for the people’s everyday activity or about tendencies of the phenomenon’s progress because all these data can be taken in the form of the statistical graphs or percentages. There are many daily questions the answers to which are hidden in the statistical data.

Black, K. (2009). Business statistics: Contemporary decision making . USA: Wiley.

Bluman, A. G. (2009). Elementary statistics: A step by step approach. New York, NY: McGraw-Hill.

Mann, P. S. (2010). Introductory statistics . Hoboken, NJ: Wiley.

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IvyPanda. (2023, October 30). Importance of Statistics in Daily Life Essay. https://ivypanda.com/essays/statistics-in-the-everyday-life/

"Importance of Statistics in Daily Life Essay." IvyPanda , 30 Oct. 2023, ivypanda.com/essays/statistics-in-the-everyday-life/.

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IvyPanda . 2023. "Importance of Statistics in Daily Life Essay." October 30, 2023. https://ivypanda.com/essays/statistics-in-the-everyday-life/.

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Writers often need to discuss numbers and statistics in their manuscripts, and it can be a challenge to determine how to represent these in the most readable way. APA 7 contains detailed guidelines for how to write numbers and statistics, and the most common are listed below. These guidelines, however, are not exhaustive and writers may need to evaluate particular instances of numbers in their own writing to determine if the guideline applies or if an exception should be made for clarity.

Numbers 

Generally, you can spell out numbers below 10 in words (seven, three), and use numerals for anything 10 and higher (10, 42). 

• You should use Arabic numerals (1, 7) instead of Roman numerals (II, XI) unless the Roman numerals are part of established terminology in your field. 
• In numbers greater than 1,000, use commas to separate groups of three digits except in page numbers, binary code, serial numbers, temperatures, acoustic frequencies, and degrees of freedom. 
• Do not add apostrophes when writing a plural of a number (the 2000s, the 70s).

Use a numeral in these cases:

• a number 10 or higher anywhere in the paper
• a number right before a unit of measurement (3 m, 24 g)
• a number denoting: mathematical functions, fractions, decimals, percentages, ratios, percentiles (2:1 ratio, 5%)
• a number denoting: time, a date, an age, a point on a scale, an exact amount of money, or a numeral (the 3 key on your keyboard, 7 years old, a 5 on the test)
• a number indicating a place in a series or a part of a book/table, if the number is after a noun (i.e., Item 4, but words are used in cases like "the fourth item")

Spell the number out in words in these cases:

• a number from 0-9 anywhere in the paper, except the specific cases above
• a number that starts a sentence, heading, or title (though this should be avoided)
• a number that is a common fraction (one half, two thirds)
• a number that is part of a common phrase (Noble Eightfold Path)

When numbers are written next to each other in a sentence, one strategy to help readers parse the sentence is to combine words and numerals (3 two-year-old owls, four 3-step plans), but rewording to separate the numbers may be the best choice for clarity in some cases. Clarity for readers is always the most important consideration.

Ordinal Numbers 

Treat ordinal numbers (3rd, fourth) the same way as other numbers, using the guidelines above. You may use a superscript or not (1 st , 1 st ), but you should maintain the same usage throughout your paper.

Decimal Fractions 

In numbers less than 1, writers may include a leading 0 before the decimal point or not. This choice is based on the maximum possible amount of the statistic:

• If the statistic can be greater than 1, use a leading 0 (0.24 in)
• If the statistic cannot be greater than 1, do not use a leading 0 (p = .042)

APA's general principle for rounding decimals in experimental results is as follows, quoted here for accuracy: "Round as much as possible while considering prospective use and statistical precision" (7th edition manual, p. 180). Readers can more easily understand numbers with fewer decimal places reported, and generally APA recommends rounding to two decimal places (and rescaling data if necessary to achieve this).

Some more specific guidelines for particular values are listed below.

One decimal place:

• standard deviations

Two decimal places:

• correlations
• proportions
• inferential statistics
• exact p values (can be reported to two or three places; when p is less than .001, write p < .001)

Statistical Copy 

These rules cover presentation of data, not accuracy of data or the best way to conduct analysis.

You can represent data in the text, in a table, or in a figure. A rule of thumb is:

• <3 numbers → try a sentence
• 4-20 numbers → try a table
• >20 numbers → try a figure

Clarity is always paramount.

When discussing statistics in common use, you do not need to provide a reference or formula.

If the statistic or expression is new, rare, integral to the paper, or used in an unconventional way, provide a reference or formula.

The purpose of reporting statistics is usually to help readers confirm your findings and analyses; as such, the degree of specificity in reporting results should follow in line with that purpose.

When your data are multilevel, you should include summary statistics for each level, depending on the kind of analysis performed. When your data are reported in a table or figure, you do not need to repeat each number in the text, but you should mention the table or figure in the text when discussing the statistics and emphasize in-text key data points that help interpret your findings. 

Use words like "respectively" or "in order" to clarify each statistic mentioned in text and their referent.

For instance:

Confidence intervals should be reported: 90% CI [ LL, UL ], with LL as the lower limit and UL as the upper limit of the interval. You do not need to repeat confidence intervals in the same paragraph or in a series when the meaning is clear and the confidence interval has not changed. When CIs follow the report of a point estimate, you do not need to repeat the unit of measurement.

Statistics uses a great deal of symbols and abbreviations (when a term can be both, the abbreviation refers to the concept and the symbol indicates a numeric value).

You do not need to define these when they represent a statistic or when they are composed of Greek letters. You do need to define any other abbreviation (such as ANOVA, CFA, SEM) in your paper. If the analysis you are performing uses multiple notation styles for symbols and abbreviations, only use one consistently throughout your paper.

Some other statistical symbol guidelines include:

• use words rather than symbols in narrative text; when you report a stats term with other mathematical symbols like = or +, use the symbol
• population parameters use Greek letters while estimators use Latin letters in italics (usually)
• uppercase, italicized N indicates the total membership of a sample; lowercase, italicized n indicates the membership of a subgroup of a sample such as a treatment group or control group
• % and currency symbols like $ should only be used with numerals (15%, $25) or in table headings and figure labels to save space
• use standard type (no italics or bold) for Greek letters, subscript and superscript identifiers, and abbreviations that are not variables such as log
• use bold type for vector and matrix symbols
• use italics for all other statistical symbols

Mathematical Copy 

For ease of reading, use spaces between elements in a mathematical expression ( a + b = c ), except in the case of a minus sign indicating a negative number which uses a space before the minus but not between the minus and the numeral.

Use subscripts first and then superscripts, except in the case of key symbols like the superscript for prime.

All equations should be punctuated to fit in the syntax of the sentence, even if they are presented on their own line.

Short, simple equations can be written in a regular line of text, with a slash (/) for fractions. Parentheses, square brackets, and braces should be used (in that order, from innermost to outermost) to indicate order of operations. Equations that do not fit vertically in the line of text should be shown on their own line. 

All displayed equations (equations on their own line) should be numbered, similarly to tables and figures, so that they can be referred to later (and simple equations may be displayed rather than written in a line of text if they will need to be referred to later by number).

In text, equations should be referred to by name (Equation 1 or the first equation are both acceptable). The equation number does not need a special label, and instead should be displayed in parentheses toward the right margin of the page:

  If a symbol in your equation cannot be entered with your word processor, use an image; otherwise, type all equations exactly as you would like them to appear in the publication.

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Researching an Argument

• Find Statistics
• Select a Topic
• Find Background Information
• Examine the Existing Argument
• Power Searching
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Suggested Guide:

• Statistics: Locate Reliable Sources of Statistical Information by Maryellen Nash Last Updated Jan 12, 2024 247 views this year

Finding statistics

• Where can I find statistics?
• General Collections of Statistics
• Sample Agency Statistics

Strategies for Finding Statistics

• Many statistics are gathered by government agencies (for example, the U.S. Department of Energy) or large organizations (for example, The American Cancer Society.) 
• Ho wev er, you can also find good statistics within books or journal, magazine or newspaper articles . 
• When you find statistics, check the source of the data .  In other words, does the book, article or website list where the statistics came from?  Do they come from a reputable or well-known source? Also take note of the date. How old is the information? If not, attempt to find statistics from a more trustworthy source.

A Google trick for finding statistics: Google site:.gov (or whatever domain you want to search) to locate only government stats.

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What’s the effect of statistics in persuasive writing?

Reframe the data to improve decision-making.

People in one study rated a disease that kills 1,286 people out of every 10,000 as more dangerous than one that kills 24.14% of the population (Yamagishi, 1997). But in fact, it’s about half as dangerous.

Why? The way you present, or frame, the information changes the way people — even experts — perceive it.

“If you tell someone that something will happen to one out of 10 people, they think, ‘Well, who’s the one?’” Paul Slovic, a University of Oregon psychologist, told Money.

Trying to help readers make a complex decision? Reframe the data so people can more easily see its meaning. Here’s how:

1. Choose frequencies, not probabilities.

People process frequencies (2 out of 100) better than percentages (2%) (Kaplan, 1986). Frequencies are effective because they:

• Demonstrate the importance of data . People weigh frequencies as more important than percentages when making decisions (Lipkus, Samsa and Rimer, 2001).
• Help people make better choices . In one study, faculty members and students at the Harvard Medical School made much better decisions when they received information about diseases and symptoms in the form of frequencies instead of probabilities (Huffrage, Lindsey, Hertwig and Gigerenzer, 2000).
• Help even experts see the situation more clearly . Forensic psychiatrists and psychologists judged a patient’s risk of being violent as much greater when it was communicated as a frequency instead of a probability (Slivic, Monahan and MacGregor, 2000).

2. Frame as a loss (or gain).

Give readers new ways to think about information by highlighting the potential gain or loss. You can frame your data as:

• Mortality vs. survival rates . The effect of dying seems to be greater when it is framed as a mortality rate of 10% than when it is framed as a survival rate of 90%. And both patients and doctors found surgery less attractive than radiation therapy when risk information was presented in terms of mortality rather than survival, despite surgery having better long-term prospects (McNeil, Pauker and Sox, 1986).
• Risk vs. reward . Consumers understood information much better, valued it more and gave it more weight in decision-making when it was framed as a loss or risk than as a reward. So “protect yourself from problems in health plans” is more effective than “get the best quality” (Hibbard, Harris-Kojetin, Mullen, Lubalin and Garfinkel, 2000).
• Loss vs. gain . In six out of seven studies, framing information as a loss was more effective than as a gain in communicating prevention, detection and treatment (Edwards, Elwyn, Covey, Matthews and Pill, 2001).
• Consider the message within the frame . Framing your message as a loss is more effective when promoting screening. Framing it as a gain is more effective when promoting prevention (Rothman, Martino, Bedell, Detweiler and Salovey, 1999).

3. Generalize a little.

In order to be as “correct” as possible, communicators often include too much information — six decimal points of precision, for instance, or data about confidence intervals.

But that actually makes important details harder to suss out. As a result, people weigh this information lower when making a decision (Hsee, 1996). So, for instance, offer an average point estimate (a score of 8) instead of a more correct one (7 to 9).

But don’t pile on the data.

To help people make better decisions, reframe the data — don’t just offer more data. ___

Source: Judith H. Hibbard and Ellen Peters, “ Supporting Informed Consumer Health Care Decisions: Data Presentation Approaches that Facilitate the Use of Information in Choice ,” Annual Review of Public Health , 2003, Vol. 24, pp. 413-33

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Essay on Statistics: Meaning and Definition of Statistics

“Statistics”, that a word is often used, has been derived from the Latin word ‘Status’ that means a group of numbers or figures; those represent some information of our human interest.

We find statistics in everyday life, such as in books or other information papers or TV or newspapers.

Although, in the beginning it was used by Kings only for collecting information about states and other information which was needed about their people, their number, revenue of the state etc.

This was known as the science of the state because it was used only by the Kings. So it got its development as ‘Kings’ subject or ‘Science of Kings’ or we may call it as “Political Arithmetic’s”. It was for the first time, perhaps in Egypt to conduct census of population in 3050 B.C. because the king needed money to erect pyramids. But in India, it is thought, that, it started dating back to Chandra Gupta Maurya’s kingdom under Chankya to collect the data of births and deaths. TM has also been stated in Chankya’s Arthshastra.

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But now-a-days due to its pervading nature, its scope has increased and widened. It is now used in almost in all the fields of human knowledge and skills like Business, Commerce, Economics, Social Sciences, Politics, Planning, Medicine and other sciences, Physical as well as Natural.

Definition :

The term ‘Statistics’ has been defined in two senses, i.e. in Singular and in Plural sense.

“Statistics has two meanings, as in plural sense and in singular sense”.

—Oxford Dictionary

In plural sense, it means a systematic collection of numerical facts and in singular sense; it is the science of collecting, classifying and using statistics.

A. In the Plural Sense :

“Statistics are numerical statements of facts in any department of enquiry placed in relation to each other.” —A.L. Bowley

“The classified facts respecting the condition of the people in a state—especially those facts which can be stated in numbers or in tables of numbers or in any tabular or classified arrangement.” —Webster

These definitions given above give a narrow meaning to the statistics as they do not indicate its various aspects as are witnessed in its practical applications. From the this point of view the definition given by Prof. Horace Sacrist appears to be the most comprehensive and meaningful:

“By statistics we mean aggregates of facts affected to a marked extent by multiplicity of causes, numerically expressed, enumerated or estimated according to reasonable standard of accuracy, collected in a systematic manner for a predetermined purpose, and placed in relation to each other.”—Horace Sacrist

B. In the Singular Sense :

“Statistics refers to the body of technique or methodology, which has been developed for the collection, presentation and analysis of quantitative data and for the use of such data in decision making.” —Ncttor and Washerman

“Statistics may rightly be called the science of averages.” —Bowleg

“Statistics may be defined as the collection, presentation, analysis, and interpretation of numerical data.” —Croxton and Cowden

Stages of Investigations :

1. Collection of Data:

It is the first stage of investigation and is regarding collection of data. It is determined that which method of collection is needed in this problem and then data are collected.

2. Organisation of Data:

It is second stage. The data are simplified and made comparative and are classified according to time and place.

3. Presentation of Data:

In this third stage, organised data are made simple and attractive. These are presented in the form of tables diagrams and graphs.

4. Analysis of Data:

Forth stage of investigation is analysis. To get correct results, analysis is necessary. It is often undertaken using Measures of central tendencies, Measures of dispersion, correlation, regression and interpolation etc.

5. Interpretation of Data:

In this last stage, conclusions are enacted. Use of comparisons is made. On this basis, forecasting is made.

Some Modern Definitions :

From the above two senses of statistics, modem definitions have emerged as given below:

“Statistics is a body of methods for making wise decisions on the face of uncertainty.” —Wallis and Roberts

“Statistics is a body of methods for obtaining and analyzing numerical data in order to make better decisions in an uncertain world.” —Edward N. Dubois

So, from above definitions we find that science of statistics also includes the methods of collecting, organising, presenting, analysing and interpreting numerical facts and decisions are taken on their basis.

The most proper definition of statistics can be given as following after analysing the various definitions of statistics.

“Statistics in the plural sense are numerical statements of facts capable of some meaningful analysis and interpretation, and in singular sense, it relates to the collection, classification, presentation and interpretation of numerical data.”

Related Articles:

• 7 Main Characteristics of Statistics – Explained!
• Use of Statistics in Economics: Origin, Meaning and Other Details
• Nature and Subject Matter of Statistics
• Relation of Statistics with other Sciences

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Students Are Likely Writing Millions of Papers With AI

Students have submitted more than 22 million papers that may have used generative AI in the past year, new data released by plagiarism detection company Turnitin shows.

A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing. (Turnitin is owned by Advance, which also owns Condé Nast, publisher of WIRED.) Turnitin says its detector has a false positive rate of less than 1 percent when analyzing full documents.

ChatGPT’s launch was met with knee-jerk fears that the English class essay would die . The chatbot can synthesize information and distill it near-instantly—but that doesn’t mean it always gets it right. Generative AI has been known to hallucinate , creating its own facts and citing academic references that don’t actually exist. Generative AI chatbots have also been caught spitting out biased text on gender and race . Despite those flaws, students have used chatbots for research, organizing ideas, and as a ghostwriter . Traces of chatbots have even been found in peer-reviewed, published academic writing .

Teachers understandably want to hold students accountable for using generative AI without permission or disclosure. But that requires a reliable way to prove AI was used in a given assignment. Instructors have tried at times to find their own solutions to detecting AI in writing, using messy, untested methods to enforce rules , and distressing students. Further complicating the issue, some teachers are even using generative AI in their grading processes.

Detecting the use of gen AI is tricky. It’s not as easy as flagging plagiarism, because generated text is still original text. Plus, there’s nuance to how students use gen AI; some may ask chatbots to write their papers for them in large chunks or in full, while others may use the tools as an aid or a brainstorm partner.

Students also aren't tempted by only ChatGPT and similar large language models. So-called word spinners are another type of AI software that rewrites text, and may make it less obvious to a teacher that work was plagiarized or generated by AI. Turnitin’s AI detector has also been updated to detect word spinners, says Annie Chechitelli, the company’s chief product officer. It can also flag work that was rewritten by services like spell checker Grammarly, which now has its own generative AI tool . As familiar software increasingly adds generative AI components, what students can and can’t use becomes more muddled.

Detection tools themselves have a risk of bias. English language learners may be more likely to set them off; a 2023 study found a 61.3 percent false positive rate when evaluating Test of English as a Foreign Language (TOEFL) exams with seven different AI detectors. The study did not examine Turnitin’s version. The company says it has trained its detector on writing from English language learners as well as native English speakers. A study published in October found that Turnitin was among the most accurate of 16 AI language detectors in a test that had the tool examine undergraduate papers and AI-generated papers.

Lauren Goode

William Turton

Eric Ravenscraft

Schools that use Turnitin had access to the AI detection software for a free pilot period, which ended at the start of this year. Chechitelli says a majority of the service’s clients have opted to purchase the AI detection. But the risks of false positives and bias against English learners have led some universities to ditch the tools for now. Montclair State University in New Jersey announced in November that it would pause use of Turnitin’s AI detector. Vanderbilt University and Northwestern University did the same last summer.

“This is hard. I understand why people want a tool,” says Emily Isaacs, executive director of the Office of Faculty Excellence at Montclair State. But Isaacs says the university is concerned about potentially biased results from AI detectors, as well as the fact that the tools can’t provide confirmation the way they can with plagiarism. Plus, Montclair State doesn’t want to put a blanket ban on AI, which will have some place in academia. With time and more trust in the tools, the policies could change. “It’s not a forever decision, it’s a now decision,” Isaacs says.

Chechitelli says the Turnitin tool shouldn’t be the only consideration in passing or failing a student. Instead, it’s a chance for teachers to start conversations with students that touch on all of the nuance in using generative AI. “People don’t really know where that line should be,” she says.

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