hypothesis definition in bangladesh

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Hypothesis - Meaning in Bengali

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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.

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On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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• Indian J Crit Care Med
• v.23(Suppl 3); 2019 Sep

An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors

Priya ranganathan.

1 Department of Anesthesiology, Critical Care and Pain, Tata Memorial Hospital, Mumbai, Maharashtra, India

2 Department of Surgical Oncology, Tata Memorial Centre, Mumbai, Maharashtra, India

The second article in this series on biostatistics covers the concepts of sample, population, research hypotheses and statistical errors.

How to cite this article

Ranganathan P, Pramesh CS. An Introduction to Statistics: Understanding Hypothesis Testing and Statistical Errors. Indian J Crit Care Med 2019;23(Suppl 3):S230–S231.

Two papers quoted in this issue of the Indian Journal of Critical Care Medicine report. The results of studies aim to prove that a new intervention is better than (superior to) an existing treatment. In the ABLE study, the investigators wanted to show that transfusion of fresh red blood cells would be superior to standard-issue red cells in reducing 90-day mortality in ICU patients. 1 The PROPPR study was designed to prove that transfusion of a lower ratio of plasma and platelets to red cells would be superior to a higher ratio in decreasing 24-hour and 30-day mortality in critically ill patients. 2 These studies are known as superiority studies (as opposed to noninferiority or equivalence studies which will be discussed in a subsequent article).

SAMPLE VERSUS POPULATION

A sample represents a group of participants selected from the entire population. Since studies cannot be carried out on entire populations, researchers choose samples, which are representative of the population. This is similar to walking into a grocery store and examining a few grains of rice or wheat before purchasing an entire bag; we assume that the few grains that we select (the sample) are representative of the entire sack of grains (the population).

The results of the study are then extrapolated to generate inferences about the population. We do this using a process known as hypothesis testing. This means that the results of the study may not always be identical to the results we would expect to find in the population; i.e., there is the possibility that the study results may be erroneous.

HYPOTHESIS TESTING

A clinical trial begins with an assumption or belief, and then proceeds to either prove or disprove this assumption. In statistical terms, this belief or assumption is known as a hypothesis. Counterintuitively, what the researcher believes in (or is trying to prove) is called the “alternate” hypothesis, and the opposite is called the “null” hypothesis; every study has a null hypothesis and an alternate hypothesis. For superiority studies, the alternate hypothesis states that one treatment (usually the new or experimental treatment) is superior to the other; the null hypothesis states that there is no difference between the treatments (the treatments are equal). For example, in the ABLE study, we start by stating the null hypothesis—there is no difference in mortality between groups receiving fresh RBCs and standard-issue RBCs. We then state the alternate hypothesis—There is a difference between groups receiving fresh RBCs and standard-issue RBCs. It is important to note that we have stated that the groups are different, without specifying which group will be better than the other. This is known as a two-tailed hypothesis and it allows us to test for superiority on either side (using a two-sided test). This is because, when we start a study, we are not 100% certain that the new treatment can only be better than the standard treatment—it could be worse, and if it is so, the study should pick it up as well. One tailed hypothesis and one-sided statistical testing is done for non-inferiority studies, which will be discussed in a subsequent paper in this series.

STATISTICAL ERRORS

There are two possibilities to consider when interpreting the results of a superiority study. The first possibility is that there is truly no difference between the treatments but the study finds that they are different. This is called a Type-1 error or false-positive error or alpha error. This means falsely rejecting the null hypothesis.

The second possibility is that there is a difference between the treatments and the study does not pick up this difference. This is called a Type 2 error or false-negative error or beta error. This means falsely accepting the null hypothesis.

The power of the study is the ability to detect a difference between groups and is the converse of the beta error; i.e., power = 1-beta error. Alpha and beta errors are finalized when the protocol is written and form the basis for sample size calculation for the study. In an ideal world, we would not like any error in the results of our study; however, we would need to do the study in the entire population (infinite sample size) to be able to get a 0% alpha and beta error. These two errors enable us to do studies with realistic sample sizes, with the compromise that there is a small possibility that the results may not always reflect the truth. The basis for this will be discussed in a subsequent paper in this series dealing with sample size calculation.

Conventionally, type 1 or alpha error is set at 5%. This means, that at the end of the study, if there is a difference between groups, we want to be 95% certain that this is a true difference and allow only a 5% probability that this difference has occurred by chance (false positive). Type 2 or beta error is usually set between 10% and 20%; therefore, the power of the study is 90% or 80%. This means that if there is a difference between groups, we want to be 80% (or 90%) certain that the study will detect that difference. For example, in the ABLE study, sample size was calculated with a type 1 error of 5% (two-sided) and power of 90% (type 2 error of 10%) (1).

Table 1 gives a summary of the two types of statistical errors with an example

Statistical errors

In the next article in this series, we will look at the meaning and interpretation of ‘ p ’ value and confidence intervals for hypothesis testing.

Source of support: Nil

Conflict of interest: None

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Definition of hypothesis noun from the Oxford Advanced Learner's Dictionary

• to formulate/confirm a hypothesis
• a hypothesis about the function of dreams
• There is little evidence to support these hypotheses.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• Her hypothesis concerns the role of electromagnetic radiation.
• Her study is based on the hypothesis that language simplification is possible.
• It is possible to make a hypothesis on the basis of this graph.
• None of the hypotheses can be rejected at this stage.
• Scientists have proposed a bold hypothesis.
• She used this data to test her hypothesis
• The hypothesis predicts that children will perform better on task A than on task B.
• The results confirmed his hypothesis on the use of modal verbs.
• These observations appear to support our working hypothesis.
• a speculative hypothesis concerning the nature of matter
• an interesting hypothesis about the development of language
• Advances in genetics seem to confirm these hypotheses.
• His hypothesis about what dreams mean provoked a lot of debate.
• Research supports the hypothesis that language skills are centred in the left side of the brain.
• The survey will be used to test the hypothesis that people who work outside the home are fitter and happier.
• This economic model is really a working hypothesis.
• speculative
• concern something
• be based on something
• predict something
• on a/​the hypothesis
• hypothesis about
• hypothesis concerning

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Impact of Economic Structure on the Environmental Kuznets Curve (EKC) hypothesis in India

Muhammed Ashiq Villanthenkodath, Mohini Gupta, … Malayaranjan Sahoo

Avoid common mistakes on your manuscript.

1 Introduction

The Environmental Kuznets Curve (EKC) that portrays the relationship between economic growth and environmental degradation is critical to address the environmental and developmental challenges faced by the countries and therefore, still remains a subject of intense research (Altintus, 2020 ). Amidst the growing threat to human health due to the COVID 19 pandemic, the hidden dangers related to climatic and ecological changes should not be ignored since the socio-economic problems often result in socio-ecological problems. High dependence on modern technology, the intensive use of fossil energy, depletion of natural resources, increased industrial activities, rapid urbanization, and globalization are taking their toll by causing environmental damage and stressing the ecosystem. This can be described as the stress of human activity on nature (Ansari et al., 2020 ; Warner et al., 2010 ) that results in an increased level of pollution, and a decline in biological resources, and food production capabilities (Bagliani et al., 2008 ). Therefore, deterioration in environmental quality has become a public concern and is increasingly gaining importance in the formulation of effective policy measures (Rahman et al., 2018 ). The relationship between environmental degradation and influencing macroeconomic factors needs to be examined carefully to design effective economic and environmental policies (Ansari et al., 2020 ; Rahman et al., 2018 ).

Maximization of ‘economic growth’ is fundamental for all economies. However, it is also important to be concerned about the causal relationship between economic growth and the environmental degradation indicators since economic growth is often accompanied by environmental degradation (Narayan & Narayan, 2010 ; Ansari et al., 2020 ). The nexus between degraded environmental quality and economic growth can better be described by the Environmental Kuznets Curve (EKC) hypothesis, which has been utilized by researchers since the early 1990s. This hypothesis has provided insights into the economic research suggesting that economies should concentrate on their growth performance and environmental issues will automatically be resolved with the progression of growth (Kaika & Zervas, 2013 ). Hence, the EKC hypothesis has been investigated in a number of ways and under different contexts by economic researchers in recent years (Wang et al., 2013 ). Some studies have opined that the EKC hypothesis is not applicable to poor countries that are operating in their early stages of economic development and are uncertain about reaching the point where the relationship between income and pollution starts to become negative. These countries often compromise their environmental standards in order to attain international competitiveness and to attract investment for achieving the much-needed economic growth. As a result, economic growth comes with worsening environmental conditions in these low-income countries (See Al-Mulali et al., 2015 ; Asici & Acar, 2015 ). But some other studies have opposed this idea (see Ulucak & Bilgili, 2018 ; Destek & Sarkodie, 2019 ) by confirming the EKC hypothesis for countries of all income groups, even for newly industrialized countries. These conflicting results in the application of the EKC hypothesis have caught the attention of economic and environmental researchers and influence them to work further on the EKC hypothesis since it is critical for policy formulation in the context of climate change and sustainable development (Destek & Sarkodie, 2019 ; Dinda, 2004 ).

Bangladesh has been identified as a fast-growing country in South Asia (Rahman et al., 2018 ). The country officially graduated to the category of a lower-middle-income country in July 2015. During the last three decades, Bangladesh has maintained an annual economic growth rate of 6.7 percent of gross domestic product (GDP) per annum. Industrial growth and urbanization have contributed much to this growth. The target was to reach and then maintain the milestone of gross national income (GNI) per capita of over US$1046. To maintain this standard, Bangladesh needs to keep the annual GDP growth rate at 7–8 percent in the next decade and the target may increase further due to the COVID 19 global pandemic-induced economic downturn since last year (BER, 2019 ; UN, 2016 ). The economic activities that are needed to achieve this growth target will impose a further cost on the environment, while Bangladesh has already scored among the lowests (29.56) in the environmental performance index of the year 2018 among 180 countries of the world (Rahman et al., 2020 ). Pollution, environmental health risks, and vulnerability to climatic change are the issues of concern for Bangladesh (WB, 2018 ). Environmental pollution imposes a multidimensional impact on ecological systems (Ansari et al., 2020 ), and hence, the ecological footprint has been considered in this study as an indicator of environmental degradation for Bangladesh.

The EKC hypothesis has been investigated in this study for Bangladesh using the ecological footprint. This study finds it relevant to evaluate the economic growth-environmental degradation nexus at the conjunction of its ongoing effort to ensure economic up gradation. Very few studies are available in relation to Bangladesh’s environment and ecology with conclusive results (Islam & Shahbaz, 2012 ; Rahman & Kashem, 2017 ; Rahman et al., 2018 ). The findings of this study are expected to contribute to the EKC literature with some policy suggestions that will help to minimize the level of environmental degradation during the period of extended economic activities. The major contributions of this research are: Firstly, it will investigate the economic growth and environmental degradation nexus in a more representative manner by using the ecological footprint (EF from hereafter) as a variable of interest which is a superior measure of environmental degradation and able to capture its multidimensional impact (Ansari et al., 2020 ). The advantage of this accounting-based indicator is that it is able to capture typically complex resource use patterns, recognize the complementarity between natural capital and economic development where humans are acknowledged as dependent on the ecosystem (Costanza, 2000 ; Moffatt, 2000 ). Therefore, analysis based on ecological footprint is relevant for the countries like Bangladesh that focus more on natural resources to extract the comparative advantage (Sarkodie & Strezov, 2018 ). This can provide better policy insights in the context of a faster pace of increase in global ecological footprint, particularly in Asia (Ansari et al., 2020 ; Danish et al., 2020 , Bilgili & Ulucak, 2019 ; Lin et al., 2018 ). Secondly, using primary energy consumption as an explanatory variable this study will assess the impact of all types of energy consumption on the environment in Bangladesh. Thirdly, adding human capital, based on education and earning capabilities, into the model, the socio-economic aspect is tried to be captured, to find out the overall impact on human ecological demand in Bangladesh. Fourthly, Robust empirical findings will unveil the dynamic impact of economic growth on the ecological degradation process and identify its causal factors to contribute to the environmental impact system (EIS) in Bangladesh which needs to be improved (Kabir & Momtaz, 2012 ).

2 Literature review

Recent papers on environmental impact assessment use the ecological footprint (EF) as a comprehensive indicator of environmental degradation and a measure of sustainable development (Caviglia-Harris et al., 2009 ; Danish et al., 2020 ; Ulucak & Bilgili, 2018 ). EF has attracted the attention of the researchers in investigations of the economic growth-environmental degradation nexus where a significant portion of studies have attempted to test the EKC hypothesis mentioned by Grossman and Kruger ( 1991 ). The inverted U-shaped EKC reveals the potential association between a country’s economic growth and ecological degradation. The EKC is considered to be a byproduct of the convergence process to the sustainable development path by the ‘green Solow model’ proposed by Brock and Taylor ( 2010 ). The rudimentary concept of the EKC elaborates that countries at their early stage of development disrupt the quality of environment through their economic activities aiming to achieve growth. These pre-industrial (primary-sector based) countries operate at the initial level (upward sloping) of the EKC. However, when continued economic growth boosts the industrial economy and growth-led increase in income approaches to achieve a threshold level of per capita income, countries (middle-income countries) arrive at the next level of the EKC. Further increases of per capita income in the post-industrial economy (mostly tertiary sector-based) lead the countries to the desired level of EKC (downward sloping) where the ecological condition starts to improve (Lawson et al., 2020 ; Suki et al., 2020 ).

The results of an empirical investigation on the EKC hypothesis vary according to environmental degradation indicators and data used (Lawson et al., 2020 ). The most recent study by Altıntaş and Kassouri ( 2020 ) has examined the EKC hypothesis using both CO 2 and EF for 14 European countries and finds only EF as a compatible tool to predict the EKC. Using EF as an environmental degradation indicator, Ansari et al. ( 2020 ) have established the EKC relationship for Central and East Asian countries while using material footprints (MFs) the relationship is established for all other Asian countries under investigation except central Asia. Examining the development dynamics of 11 newly industrialized countries the EKC hypothesis is supported for Mexico, Philippines, Singapore, and South Africa, while U-shaped relation is projected for China, India, South Korea, Thailand, and Turkey by Destek and Sarkodie ( 2019 ). In a study on randomly selected countries by income groups, Ulucak and Bilgili ( 2018 ) have confirmed the existence of the EKC hypothesis for low-income, middle-income and high-income countries. The studies of Danish and Wang ( 2019 ) and Danish et al. ( 2020 ) have found evidence in favor of the Environmental Kuznets relationship between income and EF for the Next 11 countries and BRICS countries, respectively. EKC has also been established in the Malaysian economy by Suki et al. ( 2020 ) after investigating the impact of globalization. Natural resources have been found to be helpful in supporting the EKC hypothesis, and these resources have a positive effect on the ecological footprint. The study by Hassan et al. ( 2019 ) has revealed this with a bidirectional causal link between natural resources and ecological footprints in Pakistan. Employing the production and import component of ecological footprint data, Aşici and Acar ( 2015 ) have also detected an EKC relationship between per capita income and footprint of domestic production for 116 countries. Import footprints have been found to be increasing monotonically with income in this study that focuses on the importance of environmental regulations to influence import decisions of the countries.

Using the same indicator for environmental degradation, many studies have failed to establish the EKC hypothesis. The EKC relationship has not been established for five GCC countries by Ansari et al. ( 2020a ) and neither has it been found for west, south and south-east Asian countries by Arshad Ansari et al. ( 2020b ) using EF. Aydin et al. ( 2019 ) have also failed to establish the EKC hypothesis between the types of ecological footprints and economic development for 26 European countries. The empirical investigation of this study has found that income changes affect different ecological footprints differently for various sub-footprints and there is a threshold effect of income per capita on types of ecological footprint. Zhang ( 2019 ) has failed to find any evidence of EKC for ninety-three countries of Central Asia. Investigating 15 EU countries, Destek et al. ( 2018 ) found no inverted U-relationship. In Vietnam, Al-Mulali et al. ( 2015 ) found no existence of the EKC hypothesis; rather it was found to be positive for both the short run and the long run. Wang et al. ( 2013 ) also found no evidence of EKC for 150 countries while investigating the dependence of domestic environmental performance on the characteristics of neighboring states. No evidence of the EKC relationship was found by Caviglia-Harris et al. ( 2009 ), and EF was found to be increasing at an increasing rate due to energy use in both rich and poor countries. A consumption-based line of inquiry on EKC by Bagliani et al. ( 2008 ) also found no evidence of inverted U-shape behavior for 141 countries. Rather, this study revealed an unbounded growth of environmental pressure with an increase in per capita GDP. The EKC relationship was also investigated in the context of environmental efficiency and its relation to economic growth by Halkos and Tzeremes ( 2009 ) for 17 OECD countries by applying a DEA window analysis but failed to observe inverted U. This study made a claim not only for growth, but also for the path of growth to ensure environmental protection. Sarkodie and Adams ( 2018 ) have emphasized political institutional quality for social, governmental, and economic readiness to mitigate the environmental adverse impacts.

The derogatory impact of urbanization on both ecological and material footprints is observed by the study of Ansari et al. ( 2020 ). Danish and Wang ( 2019 ) have found that urbanization increases the EF in the Next 11 countries although the negative sign of the interaction term between urbanization and real income indicates that increased income proposes the demand for the innovation of green technology, green city projects, better education, and healthcare for urban people who seek for a reduction in environmental degradation. A contradictory result about urbanization is revealed in another study by Danish et al. ( 2020 ) for BRICS countries. Nathaniel et al. ( 2019 ) have also found that urbanization improves the environmental quality in the long run in South Africa. The production and supply of renewable energy and less reliance on fossil fuel consumption have been encouraged in most of the studies. Altıntaş and Kassouri ( 2020 ) have observed this and have expressed the opinion that the behavior of economic growth in all types of environmental degradation indicators remains unaffected by the inclusion of renewable energy and fossil fuel. This study also has identified renewable energy as an environment-friendly source of economic growth, while fossil fuel consumption remains as a contributing factor to the positive relationship between environmental pressure indicators and economic growth. Similar findings of energy consumption have also been obtained by Ansari et al. ( 2020 ) for Gulf Cooperative Council (GCC) countries, Appiah et al. ( 2019 ) for selected emerging economies, Destek and Sarkodie ( 2019 ) for 11 newly industrialized countries, Danish and Wang ( 2019 ) for Next-11 countries and Destek et al. ( 2018 ) for EU countries. In the study by Ulucak and Bilgili ( 2018 ), the significant negative effect of human capital on ecological footprints has suggested education as an effective tool to overcome environmental threats. A focus on the development of human capital has also been revealed in a study on India by Ahmed and Wang ( 2019 ) and in Pakistan by Hassan et al. ( 2019 ).

While mixed results still persist in the literature regarding the EKC relationship employing EF as environmental degradation indicator, its application is found to be minimal in the context of Bangladesh. Rahman et al. ( 2018 ) employed EF as one of the socio-economic indicators in an investigation on their relationships with per capita growth in Bangladesh. Using linear, quadratic and log models, the study has confirmed a monotonic increasing relationship between growth and each of the environmental attributes. However, this study has not controlled the influence of other factors contributing to the environment. Existing studies on the EKC based on carbon emissions level in Bangladesh have provided contradictory results (see Rahman et al., 2020 ; Alam, 2014 ). Moreover, empirical investigation based only on carbon emission may lead to spurious estimates due to its link to the mixture of other effluents (Altintus & Kassouri, 2020 ). Therefore, this study has found it apposite to track the environmental degradation as portrayed by the EKC in Bangladesh using a comprehensive environmental impact indicator. It has become more relevant in response to the concern of the increased levels of economic activities for the achievement of targeted economic growth (see Rabbi et al., 2015 ). Since developing countries use their natural resources to grow and become competitive (Ulucak & Bilgili, 2018 ), this study has become motivated to use EF as an environmental degradation indicator that measures the overall demand of human activities on nature. This study expects to fill up the important shortages in the application of the EKC concept in Bangladesh by using a broader proxy of environmental degradation, EF, and at the same time identify the nexus of some socio-economic factors to the environmental degradation process in Bangladesh.

3 Rationale for variable selection in the context of Bangladesh

3.1 ecological footprint and environmental degradation.

William Rees (Rees, 1992 ) first proposed ecological footprints as an indicator of environmental degradation, and Rees and Wackernagel ( 1996 ) developed it as an assessment method (Charfeddine, 2017 ; Rees & Wackernagel, 1996 ). EF estimates the total amount of productive surface of the Earth in global hectares that is needed to supply a population with essential materials, such as water, food, timber, along with the absorption of its effluents. The overall impact on the environment is measured by this indicator (McMichael & Butler, 2011 ; Rees & Wackernagel, 1996 ). Bangladesh is ranked 29th among 189 countries in total ecological footprint ranking (NFA, 2019 ).

3.2 Urbanization and environment

Urban development and urban infrastructure systems that supply the essentials for human well-being and economic development impose a huge burden on the environment. Energy, fuel, construction, and chemical materials used in the global urbanization process cause nearly 70% of global greenhouse gas emissions, and this is mostly visible in countries with an economic growth of more than 5 percent (Fu et al., 2017 ; Pata, 2018 ). A UN report from 2016 found urban pollution growing at an alarming rate in Bangladesh (UN, 2016 ). However, urbanization is important for a country of middle income to attain targeted economic growth (WB, 2009 ). The urbanization process has got its momentum in Bangladesh for the last four decades with the dominance of the capital city Dhaka which has become the 11th largest megacity in the world (UN, 2016 ).

3.3 Energy consumption and environment

Energy use is the central to economic activity, but the use of fossil fuels causes huge emissions and is responsible for 87 percent of CO 2 emissions (Pata, 2018 ; Saboori & Sulaiman, 2013 ). Statistics showed that energy demand growth was 1.5 percentage points higher in 2018 relative to the average of the previous 5 years and it was satisfied mostly (three-quarters of total demand) by fossil fuel resulting in a 1.4 percentage point higher CO 2 emission in the year 2018 (BP, 2019 ). Bangladesh is an energy deficit country, and in many studies, the management of the energy sector has been said to be poor (Alam et al., 2012 ). The major sources of energy in Bangladesh are natural gas (more than half is used for electricity generation), petroleum, coal, and hydro-power (BBS, 2018 ). At present, the country’s emission level is negligible in its contribution to global climate change.

3.4 Trade openness and environment

Trade openness has an impact on the environment in three ways: technique, scale, and composition effects. If trade openness brings positive technical effect in the production process by introducing environmentally friendly and minimum emission technologies, the trade-induced technique surpasses the composition and scale effects and implies a net reduction to environmental degradation (Nazir et al., 2018 ).

Trade openness brings faster economic growth for Bangladesh (Manni et al., 2012 ). Readymade garments and knitwear are significant contributors (81%) of total export earnings. Many studies have found trade openness to be harmful for the lower and middle-income countries (Le et al., 2016 ; Van Tran, 2020 ). However, Bangladesh imports raw materials, engages in Cut, Make, and Trim (CMT) activities, and finally exports finished goods that are highly labor-intensive. Therefore, it can be expected that the RMG sector still remains a relatively clean and low emissions industry for Bangladesh (Oh et al., 2018 ).

3.5 Human capital and environment

Human activity and environmental degradation are interlinked (UNESCO, 2009 ). For the past several centuries, human activity has been responsible for increased pollution in the air, water, and soil and alteration of the earth’s climatic condition by disrupting the ecosystem, and depleting the natural stock of renewable energy (Harte, 2007 ). However, human capital has the potential to increase productivity and efficiency to implement green technology that can establish a positive relationship between human capital and environment (Desha et al., 2015a ; Bano et al., 2018 ; Ahmed & Wang, 2019 ). Human capital is an important agent to combine economic growth with improved environmental quality (Ahmed & Wang, 2019 ; Desha et al., 2015a , 2015b ). Bangladesh has been ranked 99th out of 124 countries in regard to human capital, while its neighboring countries, Sri Lanka and Bhutan, ranked above Bangladesh (Ahmed & Wang, 2019 ).

4 Methodology

4.1 data and model specification.

Annual data are used in this study that spans over four decades from 1972 to 2018 for Bangladesh. The stochastic impacts regression that captures supply side analysis for controlling several pressures (economic, demographic and institutional) on environmental quality, STRIPAT, has been employed in this study. The structural model of STIRPAT was derived from IPAT (Gani, 2021 ) which measures the impact of human-induced economic activities on the environment and stipulates environmental effects as the multiplicative product of three key driving forces: population, affluence (per capita consumption or production) and technology (impact per unit of economic activity) where I = PAT (York et al., 2003 ). IPAT was reformulated by Dietz and Rosa ( 1994 ) as a stochastic form named STIRPAT and can be specified as follows,

where I represents environmental impact, P represents population size, A reflects affluence and T represents technology. \(\alpha\) is the constant, \(\beta_{1} ,\beta_{2} , \beta_{3}\) are the exponents of P , A , and T, respectively. \(\mu\) is the error term. An important feature of the STIRPAT model is that it allows for the investigation of the EKC (Gani, 2021 ). In this study, we have used the core idea of STIRPAT/IPAT to find the EKC that will project the impact on environmental quality due to affluence and other anthropogenic driving forces. In this study, affluence is represented by per capita GDP, demographic variable is captured by human capital and technology is captured by trade openness based on recent studies (see Halliru et al., 2020 ). Since trade facilitates the import of advanced technology from developed to developing countries, increased level of trade openness indicates technological diffusion, better skill and better utilization of inputs (Halliru et al., 2020 ; Kwakwaet al., 2020 ). Taking into consideration, the two other important factors responsible for environmental degradation namely urbanization and energy consumption Eq. ( 1 ) can be modified as follows:

Here, \({\text{ed}}_{{\text{t}}}\) represents the environmental degradation over time. It is considered as the ecological footprint (total) per capita that tells us how much nature is used (NFA, 2019 ). The explanatory variables, gdp , urb, eng, top, hc, represent GDP per capita, urbanization level, energy consumption level, trade openness, and human capital. Incorporating the square term of \(gdp_{t}\) , this study contains the EKC hypothesis in the model. The data on GDP (GDP per capita at constant 2010 US$), urbanization (percentage of total population), and trade openness (trade-GDP ratio) have been collected from World Development Indicators (WDI, 2019 ); data of EF, primary energy consumption (kg of oil equivalent per capita) and human capital (Index of Human Capital) are extracted from the Global Ecological Footprint Network (NFA, 2019 ), BP Statistics (BP, 2019 ), and Penn World Table 9.0, respectively. All the data are transformed into natural logs to control the variance and produce more efficient and consistent results (Mrabet & Alsamara, 2017 ; Sinha & Shahbaz, 2018 ; Zafar et al., 2019 ). Adding the squared GDP to capture the EKC, the empirical equation can be modeled as follows:

Here, \(lnef_{t}\) is the natural log of the ecological footprint (total) per capita and is considered as a proxy for environmental degradation, \(ed\) , following Hassan et al. ( 2019 ) and Ulucak and Bilgili ( 2018 ). The other explanatory variables are the natural log transformation of the explanatory variables mentioned above. \(\varepsilon_{t}\) is the error term that is distributed normally with zero mean and constant variance. Parameters \(\beta_{1} ,\beta_{2} ,\beta_{3} ,\beta_{4} ,\beta_{5} , \beta_{6}\) represent the elasticity of the explanatory variables. Correspondingly, in order to predict the EKC relation in the model, the sign of \(\beta_{1}\) is expected to be positive and the sign of \(\beta_{2}\) is expected to be negative. The signs of these two coefficients are jointly expected to project the inverted U-shaped ‘Kuznets curve,’ that reflects the relative power of scale versus technique effects (Grossman & Krueger, 1991 ). But if the signs are altered, a U-shaped relationship will be seen between environmental degradation and economic growth as well. The expected sign for \(\beta_{3}\) is positive under the consideration that more urbanization will lead to a higher demand for urban ecology and energy consumption that in turn causes more damage to the environment. Evaluating the present trend of energy consumption, it has been predicted that the increase in primary energy consumption will increase the emission of polluting gases and substances to the environment. Therefore, the expected sign of \(\beta_{4}\) is positive. The sign of trade openness can be either positive or negative depending on the nature of trade and by the nature of technology transferred to the economy via open trade with the global economy. If the economy benefits from efficient technology, the \(\beta_{5}\) can be negative and contribute positively to the environment; otherwise, a positive \(\beta_{5}\) increases environmental degradation either by relocation of polluting industries from developed economies or due to a lack of implementation of environmental law, or for both of the reasons (Grossman & Krueger, 1991 ; Mrabet & Alsamara, 2017 ). Environmental issues are mostly human-induced, and humans have the potential to reduce the ecological footprint by enhancing the quality of human capital. (Ahmed & Wang, 2019 ; Bano et al., 2018 ; Zafar et al., 2019 ). Hence, the expected sign of \(\beta_{6}\) is negative.

4.2 Econometric approach

4.2.1 unit-root and co-integration tests.

The first step in empirical analysis is to check for unit roots in the variables. In order to obtain a reliable regression result, unit root properties of the variables need to be examined. Though the planned model for application, Autoregressive Distributive Lag (ARDL) model, does not necessitate all the variables to be integrated in order of unity, it is important to confirm that none of the variables is integrated to the order more than one to avoid spurious F-statistic (Pesaran et al., 2001 ). Therefore, in order to identify the integration order of the variables the present study has applied various unit root tests, viz Augmented-Dickey–Fuller (ADF) test (Dickey & Fuller, 1979 ) and Phillips–Perron (PP) unit root test (Phillips & Perron, 1988 ). The unit root tests were conducted under two specifications: intercept and intercept with trend in the model. However, in order to address the non-reliability problem in terms of sensitivity to size and to account for the structural break in time series, a Zivot–Andrew (Z-A) unit root test (Zivot & Andrews, 2002 ) was also performed.

The presence of a structural break has been ignored in the standard co-integration test approach. In order to get rid of this problem, the Gregory Hansen (G-H) co-integration test is applied. This test assumes that there is the presence of one structural break in the co-integration vector (Gregory & Hansen, 1996 ). The G-H test considers break in trend and intercept. Three alternative unit root tests (ADF, Za, and Zt) are conducted to review the possible shifts in the co-integrating vector.

4.2.2 The ARDL estimation

Since the study is interested in exploring the long-run relationship among the variables, the Autoregressive Distributed Lag (ARDL) bounds testing method of co-integration has been used after being influenced by the study of Hassan et al., ( 2019 ) and Rahman and Kashem ( 2017 ). Pesaran et al. ( 2001 ) developed this model, and it provides the best econometric method for estimating short-run and long-run estimates. The ARDL method is advantageous with accurate estimation techniques for small samples of data, and it is also free from endogeneity problems. This method of co-integration can be used without much care to the stationary level of variables whether they are integrated to I(0), I(1) or a mixture of the two (Hassan et al., 2019 ; Pesaran et al., 2001 ). The distinction between short-run and long-run effects of the explanatory variables on the dependent variables is shown simultaneously in this model (Al-Mulali et al., 2015 ; Nazir et al., 2018 ; Pesaran et al., 2001 ). The ARDL technique is employed in this study as Eq.  3 .

This is the general dynamic specification of the ARDL model. D represents the dummy variable that captures structural breaks in the model. This model uses the variables, their lags and dynamics, to estimate the long-run co-integration among the variables. Here, \(\beta_{1}\) – \(\beta_{7}\) represents the short run while \(\alpha_{1}\) – \(\alpha_{7}\) correspond to the long-run relationship.

In the bound test of the ARDL approach, the critical value of the F-test statistic is provided. The null hypothesis of the no-cointegration relationship is not rejected if the calculated F-test statistic lies below the lower bound I(0), is rejected if it lies above the upper bound I(I), and is concluded to be inconclusive if the statistic falls within the lower and upper bounds. Therefore, the null hypothesis of no co-integration relationship \(H_{0} = \alpha_{1} = \alpha_{2} = \alpha_{3} = \alpha_{4} = \alpha_{5} = \alpha_{6} = \alpha_{7} = 0\) is tested against the alternative hypothesis \(H_{1} \ne \alpha_{1} \ne \alpha_{2} \ne \alpha_{3} \ne \alpha_{4} \ne \alpha_{5} \ne \alpha_{6} \ne \alpha_{7} \ne 0\) .

The optimal lag order for the variables is chosen following the Akaike Information Criteria (AIC) to estimate the level of relationship in the ARDL specification. The cumulative (CUSUM) test of recursive residuals and cumulative sum of squares (CUSUMQ) test of recursive residuals are performed to check the stability of the estimated model, while the available diagnostic check assesses the sensitivity of the model.

To get the path of relationship among the variables, the Vector Error Correction Model (VECM)-based Granger Causality analysis is performed. To this end, the Vector Error Correction Model (VECM) is estimated as follows:

Here, \(\theta\) measures the speed of adjustment. It shows the speed at which any deviation from equilibrium due to shock in the short-run returns to the long-run equilibrium.

The VECM Granger causality method investigates both the long-run and short-run causal relationship among the variables. Short-run causality is tested with the support of the Wald test, while the log-run causality is evident through the sign of ECM and its statistical significance.

5 Empirical results

The empirical results indicate that the data series are all stationary either at level or at their first differences in both the tests ADF and PP. This unit root test result confirms that there is no integration in I(2) and the order of integration combines both level and first difference (either in intercept or trend and intercept). The results of the unit root are presented in Table 9 in the appendix.

By Zivot–Andrew (Z-A) unit root test, all the variables are stationary at level and some variables are stationary at first difference. The Z-A unit root test results with significant break year are presented in Table 10 in the appendix. This result makes it reasonable to use the ARDL bound test approach, as supported by Pesaran et al. ( 2001 ), to find out the long-run relationship among the variables.

To examine the robustness of the long-run relationship comprising structural break, the Gregory-Hansen (G-H) co-integration test is employed. The result of the co-integration test is presented in Table 1 , and a significant structural break is reported in the year 2007.

The G-H method checks three alternate unit root tests (ADF, Za, and Zt), and all the test statistics reject the null hypothesis of no co-integration and select 2007 as the possible break year. Two possible reasons for this break point are the global recession and catastrophic natural disaster ‘Sidre’ cyclone in the year 2007 that imposed huge cost to ecology, economy, and human health in Bangladesh. To tackle the problem of structural break identified in the series, the ARDL model is estimated including the structural break in year 2007. The ARDL bound test result finds co-integration based on the value of the F-statistic. The null hypothesis of no co-integration is strongly rejected for the ecological footprint, economic growth, urbanization, energy consumption, trade openness, and human capital, which suggests for carrying out the long-run estimation procedure in order to establish the association between ecological footprint and selected socio-economic factors. The values of F-statistic calculated from the equations are presented in Table 2 . The F-statistic values and their significance levels have confirmed that there is a long-run effect in the model.

The long-run coefficients of economic growth, urbanization, energy consumption, trade openness and human capital on EF are reported in Table 3 . Based on the observed result, the study has confirmed the EKC hypothesis for the long run in Bangladesh as the coefficients of GDP and GDP square are found to be positive and negative, respectively, and statistically significant. The empirical result also finds urbanization and human capital as significant factors in the context of environmental concern in Bangladesh. An increased level of urbanization increases the ecological footprints in the long run, whereas development of human capital reduces the environmental degradation process. The model satisfies important diagnostic tests and passes both the CUSUM and CUSUM square test for model stability Fig.  1 and 2 .

Plot of cumulative sum of recursive residuals

Plot of cumulative sum of squares of recursive residuals

The short-run dynamic results are reported in Table 4 . The signs of the coefficients of GDP and square of GDP endorse the existence of the EKC hypothesis in the short run with strong statistical significance. In line with the empirical finding for the long run, both urbanization and human capital contribute significantly to the environmental degradation process in the short run, while urbanization contributes negatively and human capital contributes positively to the environment. The sign of co-integration term is negative and statistically significant at 1% level of significance. This confirms the long-run relationship among the variables. This value confirms that the change in ecological footprint from short run to long run is corrected annually in a significant manner. The diagnostic test results confirm the validity of the estimated model.

The result of ARDL model is further verified with Fully Modified Ordinary Least Squares (FMOLS) test as robustness check. Table 5 presents the FMOLS statistics of the EF model for the long run.

The results in Table 5 validate the long-run results of ARDL model and confirm the existence of the EKC. The high value of adjusted R-squared shows the strong fit of the variables in the long-run model.

5.1 Granger causality test result

The results from the causality test are presented in Table 6 . In the long run, unidirectional causal links run from GDP to ecological footprint (similar to the finding of Nathaniel et al., 2019 ), from urbanization to ecological footprint and from trade openness to ecological footprint in both short run and long run. A causal link also runs from human capital to ecological footprint with no feedback that is consistent with the result of Ahmed and Wang ( 2019 ). Bidirectional causal relations are found from GDP to urbanization, and vise-versa. In the short-run, unidirectional causality runs from urbanization, energy consumption and trade openness to human capital and from trade openness to GDP.

6 Discussion

The practical implication of the empirical results is explained in this section. The EKC hypothesis is established by empirical investigation in the context of Bangladesh for both the short-run and the long-run when the ecological footprint is considered as a degradation indicator of the environmental quality. This result is consistent with the findings of Altıntaş and Kassouri ( 2020 ), Destek and Serkodie ( 2019 ) and Nathaneal et al. ( 2019 ) but contrary to the findings of Al-Mulali et al. ( 2015 ) and Zhang ( 2019 ) in long-run and with the findings of Destek and Sarkodie ( 2019 ), Ulucak and Bilgili ( 2018 ) and Ahmed and Wang ( 2019 ) in short-run analysis. Results indicate that Bangladesh belongs to the earlier stage of development and operates at the initial level (rising part) of the EKC, where economic activities to achieve growth are damaging the environment. Like other developing countries, Bangladesh has not yet attained full-fledged industrial development and it is evident by the large share of agriculture and service sector to the GDP (BER, 2019 ). However, the industrial sector is developing in Bangladesh which is evident by the increasing share of industry to the GDP. Continued economic growth will increase per capita income and eventually will achieve a threshold level. A further increase in income will start to improve the environmental quality in Bangladesh. The empirical finding of this study can be analyzed together with the pattern of structural economic change in the country. The dominance of agriculture is declining in Bangladesh in terms of its share to GDP by the steady growth of the industrial and service sectors. As the country tries to build its industrial base, the level of pollution increases. However, if the country becomes capable of continuing growth, cleaner technology will be adopted with better resource allocation that will slow the pace of environmental degradation. Furthermore, the growth in Bangladesh leads to service-oriented economies which have the potential for greater environmental gains.

Urbanization reduces environmental quality which focuses on the fact that the current pace of urbanization is not advantageous for Bangladesh in consideration of the environment. This urbanization result is in line with the findings of Danish and Wang ( 2019 ) and Al-Mulali et al. ( 2015 ) but opposed to the results revealed by Danish et al. ( 2020 ) and Nathaniel et al. ( 2019 ). Urbanization has a unidirectional causal link with EF but a bidirectional causal link with economic growth. Therefore, despite its huge potential, the unplanned nature of urbanization in Bangladesh has not yet become the source of positive externality in terms of better opportunity and income. In contrast, the empirical result finds the development of human capital as an improvement to the environmental quality and this result is similar to that of Ahmed and Wang ( 2019 ) and Ulucak and Bilgili ( 2018 ) in the long-run and with Hassan et al. ( 2019 ) in the short-run analysis. The significant positive role of human capital against environmental degradation indicates that human development is very crucial from the perspective of environment since human capital can contribute to the technical research on environment and to the increase in the level of awareness. HC that is measured in years of schooling and returns to education can play a significant role in environmental conservation through increased knowledge and income which provide not only the financial solvency to use environment-friendly and renewable technology, but also an increased level of awareness and pro-environmental activities. The causality test result of this study empirically establishes income, urbanization, and human capital as the strong causal factor for environmental degradation in Bangladesh.

7 Conclusion and policy suggestions

This study has investigated the EKC hypothesis in Bangladesh. To this end, this study examines the nexus between the ecological footprints, economic growth, urbanization, energy consumption, trade openness and human capital with particular emphasis on the EKC over the period 1972–2018. The ARDL approach with structural breaks is employed to explore the long-run association among the variables. The result indicates that all the variables are co-integrated. The study confirms the existence of the EKC hypothesis in Bangladesh, both in the short-run and in the long-run employing the index of ecological footprint that captures both the direct and indirect impacts of production and consumption activities on the environment (see Ulucak & Bilgili, 2018 ). This result supports the structural change in the economy and technical advancement of the country (see Danish et al., 2020 ; Sarkodie & Adams, 2018 ). The empirical results indicate that the present level of economic activity can be continued to achieve growth and this will not impose much cost to the ecology under the existing environmental management system. The initial increase in ecological footprint will tend to diminish with the maturity of economic growth. This result provides insights for targeted policymaking and implementation of environmental measures to extract the economic potential so that the growth target can be achieved without much damage to the ecology. The causal relation from economic growth to environmental degradation indicates that measures that are taken by the government to enhance economic growth and reduce environmental degradation in Bangladesh must be continued to diminish both economic and environmental challenges.

Unplanned urbanization is causing an environmental crisis in Bangladesh (Rahman & Kashem, 2017 ). This study finds urbanization to be the significantly contributing factor to the environmental degradation in the long-run and short-run. This causality test result of urbanization with economic growth demonstrates that planned urbanization will put forward the demand for the innovation of green technology, green city development projects, better education, and health care for urban people that will help to prevent downturns in environmental quality and promote economic growth. Proper urban planning can be suggested that will design the safe accommodation of increased urban population with better services and put forward the green development agenda in urban areas.

The study has found human development as contributing to the reduction in environmental degradation since human capital is capable of capturing the values and priorities of ecology and able to use the natural resources in a sustainable manner. Bangladesh has attained success in educational attainment. However, immense potential is left to improve its human capital as compared to other developed countries. Improvement in the quality of education is required for creating better human capital. This can help to identify the causes and understand the consequences of climatic change and environmental degradation with positive motivation to mitigate both of these (UNESCO, 2009 ). Therefore, spending in education and R&D can be suggested to develop human capital able to contribute to a reduction in environmental degradation. Besides these, since agriculture, forestry, and land use are categorized as the second contributor to greenhouse gas emissions (IPCC, 2016 ; Sarkodie & Strezov, 2018 ), Bangladesh should adopt sustainable agriculture development strategies accompanying technological advancement and scientific innovation in order to avoid environmental degradation.

Bangladesh is rich in biodiversity (BER, 2019 ). Moreover, the existence of the EKC hypothesis in the short-run and long-run implies that Bangladesh has the potential to achieve economic growth without a deterioration in the ecology and environment. However, stringent environmental conservation and pollution control measures must be continued to maintain the ecological footprint at a lower level vis-a-vis economic activities at the targeted level.

Abbreviations

Augmented dickey fuller

Autoregressive distributive lag model

Bangladesh bureau of statistics

British petroleum

Bangladesh economic review

Coronavirus disease of 2019

Cumulative sum of recursive residuals

Cumulative sum of squares of recursive residuals

Error correction model

• Ecological footprint

Environmental impact system

Environmental kuznets curve

Environmental performance index

Gulf cooperative council

Gross domestic product

Global footprint network

Gross national income

Intergovernmental panel on climate change

Material footprint

Phillips–Perron

United Nations educational, scientific and cultural organization

World development indicator

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Appendix 5: Highlights of the study

Empirical evidence in favor of Environmental Kuznets Curve hypothesis in Bangladesh.

Causal link from economic growth to environmental degradation in Bangladesh.

Urbanization to environmental degradation is linked in both short run and long run.

Economic growth with careful environmental measures and policies.

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Sultana, N., Rahman, M.M. & Khanam, R. Environmental kuznets curve and causal links between environmental degradation and selected socioeconomic indicators in Bangladesh. Environ Dev Sustain 24 , 5426–5450 (2022). https://doi.org/10.1007/s10668-021-01665-w

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• Whorfian hypothesis
• null hypothesis
• nebular hypothesis
• counter - hypothesis
• planetesimal hypothesis

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This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

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Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 16 Apr. 2024.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Mathematics Maths Formulas Branches of Mathematics

Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym:   proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

• It should never be written as a question.
• You should be able to test it in the real world to see if it’s right or wrong.
• It needs to be clear and exact.
• It should list the factors that will be used to figure out the relationship.
• It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
• It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
• It should be written in as simple a way as possible so that everyone can understand it.
• It must explain what happened to make an answer necessary.
• It should be testable in a fair amount of time.
• It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

• Patterns of similarity between the phenomenon under investigation and existing hypotheses.
• Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
• The formulations are derived from accepted scientific theories and proposed by researchers.
• In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
• Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

• Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
• Students who experience test anxiety before an English examination will get lower scores than students who do not experience test anxiety.
• Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

• Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
• Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
• Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

• The crop yield will go up a lot if the amount of fertilizer is increased.
• Patients who have surgery and are exposed to more stress will need more time to get better.
• Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

• The test scores of two groups of students are very different from each other.
• There is a link between gender and being happy at work.
• There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

• Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
• The results of a memory recall test will not reveal any significant gap in performance between children and adults.
• There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

• There is a link between how many hours you spend studying and how well you do in school.
• Drinking sugary drinks is bad for your health as a whole.
• There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

• A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
• It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
• The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
• A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

• The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
• Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
• Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
• Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
• Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
• The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
• The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

• Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
• Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
• Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
• Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
• Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
• Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
• Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
• Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

Choose the best answer. 

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Further Reading

• RNA-DNA World Hypothesis
• BYJU’S. (2023). Hypothesis. Retrieved 01 Septermber 2023, from https://byjus.com/physics/hypothesis/#sources-of-hypothesis
• Collegedunia. (2023). Hypothesis. Retrieved 1 September 2023, from https://collegedunia.com/exams/hypothesis-science-articleid-7026#d
• Hussain, D. J. (2022). Hypothesis. Retrieved 01 September 2023, from https://mmhapu.ac.in/doc/eContent/Management/JamesHusain/Research%20Hypothesis%20-Meaning,%20Nature%20&%20Importance-Characteristics%20of%20Good%20%20Hypothesis%20Sem2.pdf
• Media, D. (2023). Hypothesis in the Scientific Method. Retrieved 01 September 2023, from https://www.verywellmind.com/what-is-a-hypothesis-2795239#toc-hypotheses-examples
• Rashid, M. H. A. (Apr 20, 2022). Research Methodology. Retrieved 01 September 2023, from https://limbd.org/hypothesis-definitions-functions-characteristics-types-errors-the-process-of-testing-a-hypothesis-hypotheses-in-qualitative-research/#:~:text=Functions%20of%20a%20Hypothesis%3A&text=Specifically%2C%20a%20hypothesis%20serves%20the,providing%20focus%20to%20the%20study.

©BiologyOnline.com. Content provided and moderated by Biology Online Editors.

Last updated on September 8th, 2023

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• Scientific Methods

What is Hypothesis?

We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.

A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.

Characteristics of Hypothesis

Following are the characteristics of the hypothesis:

• The hypothesis should be clear and precise to consider it to be reliable.
• If the hypothesis is a relational hypothesis, then it should be stating the relationship between variables.
• The hypothesis must be specific and should have scope for conducting more tests.
• The way of explanation of the hypothesis must be very simple and it should also be understood that the simplicity of the hypothesis is not related to its significance.

Sources of Hypothesis

Following are the sources of hypothesis:

• The resemblance between the phenomenon.
• Observations from past studies, present-day experiences and from the competitors.
• Scientific theories.
• General patterns that influence the thinking process of people.

Types of Hypothesis

There are six forms of hypothesis and they are:

• Simple hypothesis
• Complex hypothesis
• Directional hypothesis
• Non-directional hypothesis
• Null hypothesis
• Associative and casual hypothesis

Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

Examples of Hypothesis

Following are the examples of hypotheses based on their types:

• Consumption of sugary drinks every day leads to obesity is an example of a simple hypothesis.
• All lilies have the same number of petals is an example of a null hypothesis.
• If a person gets 7 hours of sleep, then he will feel less fatigue than if he sleeps less. It is an example of a directional hypothesis.

Functions of Hypothesis

Following are the functions performed by the hypothesis:

• Hypothesis helps in making an observation and experiments possible.
• It becomes the start point for the investigation.
• Hypothesis helps in verifying the observations.
• It helps in directing the inquiries in the right direction.

How will Hypothesis help in the Scientific Method?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Formation of question
• Doing background research
• Creation of hypothesis
• Designing an experiment
• Collection of data
• Result analysis
• Summarizing the experiment
• Communicating the results

Frequently Asked Questions – FAQs

What is hypothesis.

A hypothesis is an assumption made based on some evidence.

Give an example of simple hypothesis?

What are the types of hypothesis.

Types of hypothesis are:

• Associative and Casual hypothesis

State true or false: Hypothesis is the initial point of any investigation that translates the research questions into a prediction.

Define complex hypothesis..

A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.

Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

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