1133 LearnersLast updated on June 18th, 2025
Null Hypothesis
The null hypothesis (H0) assumes that there is no effect or difference in an experimental result. It is the starting point for many scientific experiments and tests. For instance, it can help a scientist decide whether or not they should continue testing their new medicine. The word ‘null’ in its literal sense means 0 (zero), and ‘hypothesis’ indicates a proposed explanation based on limited evidence, forming a foundation for further research.
What is Null Hypothesis?
In statistics, the null hypothesis asserts that there is no difference between variables or groups. It is one of the two hypothesis regarding a population (entire group that you are studying or making conclusions about) that are mutually exclusive.
The two different types of hypotheses are the null and alternative hypotheses. They are mutually exclusive because only one hypothesis can happen at any given time. Rejecting the null hypothesis is equivalent to accepting the alternative hypothesis.
- Null Hypothesis (H0): There is no effect or difference in the population.
- Alternative Hypothesis (H1): There is an effect or difference in the population.
The null hypothesis analysis does not necessarily mean it is always correct. In fact, rejecting a null hypothesis results in more fascinating finds. So, when to reject a null hypothesis? Let’s understand this step-by-step:
Step 1: Researchers put forth a hypothesis regarding a new medicine or math theorem.
Step 2: Researchers must analyze the data once it is gathered. This is done to see if the collected data aligns properly with the null hypothesis.
P-value: A statistical measure that helps decide whether to reject the null hypothesis.
Step 3: When the experiment gives sufficient result, like whether there was an effect or difference in the test, you can reject the null hypothesis.
Step 4: The test results of the population can have only one result, that is, either rejecting the null hypothesis (accepting the alternative hypothesis) or failing to reject the null hypothesis (accepting the null hypothesis).
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What are the Tests For Null Hypothesis?
There are two main methods for statistically assessing the null hypothesis. They are: Hypothesis testing and significance testing. The null hypothesis is a theoretical hypothesis based on limited data. Therefore, it is important that it has to be tested further to determine its accuracy.
There are two types of tests for null hypothesis, they are:
Significance Testing:
It is a test that aims to reject the null hypothesis and to accept the alternative hypothesis. The purpose of the testing is to determine how strongly the evidence contradicts the hypothesis test results.
Step 1: If our assumption is null hypothesis, we should validate its prediction using significance testing.
Step 2: First calculate the test statistics and find the p-value.
Step 3: Compare the p-value and the significance level to decide if you should accept or reject the null hypothesis.
Step 4: The null hypothesis can be rejected if the p-value you got is lesser than the significance level . However, if the p-value we have is greater than the significance level, then we simply cannot reject the null hypothesis.
Hypothesis Testing:
In this method, we use the data that we gathered from a sample to draw conclusions about a larger and similar population.
Step 1: Identifying the hypothesis as null hypothesis.
Step 2: Observing and using statistical data to decide whether to reject or fail to reject the null hypothesis based on evidence.
Step 3: There can be two errors that happen while doing this. Sometimes we reject the null hypothesis when the result is true. Or accepting the null hypothesis, when the result is false.
Difference Between Null Hypothesis and Alternative Hypothesis
Let’s understand the difference between null hypothesis and alternative hypothesis in detail:
- The null and alternative hypotheses are mutually exclusive.
- The null hypothesis states that there is no difference or effect in the population tested. Whereas, the alternative hypothesis states that there is difference or effect in the population tested.
- So, null hypothesis is the opposite of alternative hypothesis.
- The null hypothesis is represented by H0, while the alternative hypothesis is symbolized as H1.
- For the null hypothesis, the result after the test will be H0: μ1= μ2. For the alternative hypothesis, the test result is H1: μ1 μ2.
- In a null hypothesis, the observed population parameters and variables remain the same across different samples. But in an alternative hypothesis, we can see a huge difference between the observed parameters and the variables across the samples.
Real Life Applications of Null Hypothesis
The null hypothesis plays a crucial role in various real-life fields, helping researchers, businesses, and scientists get data-driven results. It provides us with a platform to test if an observed effect is random or not. Below are some practical applications of the null hypothesis in different industries.
Medical Field:
In medical sciences, if a new drug is introduced, its effectiveness is put to test using null hypothesis testing. If the results show a significant improvement, they reject the null hypothesis and accept that the drug works.
Education:
In teaching methodology, comparing two teaching methods to see which one improves student performance using null hypothesis.
Business and Marketing:
In a new marketing advertisement, they check whether the advertisement increases sales through null hypothesis.
Environmental Studies:
Environmentalist check for pollution in different areas and if found any significant difference, they reject the null hypothesis.
Common Mistakes of Null Hypothesis and How to Avoid Them
Making mistakes is common, especially while conducting hypothesis testing. Hence, it is important to avoid such common mistakes, which can lead to wrong conclusions. Take a look at the below-mentioned common mistakes and ways to tackle them:
Mistake 1
Misinterpreting “Failing to Reject” as “Proving True”.
Failing to reject the null hypothesis does not mean it is true, it only means there isn’t enough evidence to reject it. Always state that there is insufficient evidence rather than confirming the null hypothesis as fact
Mistake 2
Using a Small Sample Size
A small sample might not represent the entire population, leading to inaccurate results. Always use an adequate sample size to ensure reliable conclusions.
Mistake 3
Ignoring Assumptions of Statistical Tests
Many tests assume normality, equal variances, or independent samples. Check these assumptions before running the test to avoid misleading results.
Mistake 4
Confusing Statistical Significance with Practical Importance
A small p-value does not always mean a result is meaningful in real life. Consider the effect size and real-world relevance, along with statistical significance.
Mistake 5
Conducting Multiple Tests Without Adjusting for Errors
Running many tests increases the chance of finding a false positive. Use corrections like the Bonferroni correction to control for errors when performing multiple comparisons.
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Solved Examples for Null Hypothesis
Problem 1
A school wants to test if a new teaching method improves student test scores compared to the old method. Does the new method make a difference?
According to the null hypothesis H0, the new teaching method has no effect on students’ test scores.
Explanation
The school collects test scores from students using both methods.
If statistical analysis shows a significant difference in scores, the null hypothesis is rejected, indicating that the new method is effective.
Problem 2
A pharmaceutical company claims its new painkiller has the same effect as the existing one. Is the new painkiller more effective?
The null hypothesis H0 states that the new painkiller has the same effect as the existing one.
Explanation
The company conducts a clinical trial, comparing pain relief levels in patients using both drugs.
If the new drug shows significantly better results, the null hypothesis is rejected, proving its effectiveness.
Problem 3
If a coin is flipped 100 times, how do we test if it is fair?
The null hypothesis states that the coin is fair, meaning heads and tails occur equally (50% each).
Explanation
If the results show a major imbalance, statistical analysis determines whether this is due to chance or if the coin is biased.
If the imbalance is significant, the null hypothesis is rejected.
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FAQs Related to Null Hypothesis
1.Why is the null hypothesis important in research?
2.Can the null hypothesis ever be proven true?
3.What happens if the p-value is greater than 0.05?
4.How do scientist decide whether to reject the null hypothesis?
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Jaipreet Kour Wazir
About the Author
Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
Fun Fact
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!
