Summary
The default assumption of no effect or difference, which data may lead us to reject.
In depth
Statistical tests estimate the probability of the observed data if the null is true. Rejecting the null does not prove the alternative; type I error is a false positive, type II a false negative.
Failing to reject the null is not proof of no effect; the study may be underpowered. Report effect sizes with confidence intervals.
Remember: p < 0.05 means a result at least this extreme would occur less than 5 percent of the time if the null were true.
The null hypothesis (H0) states that there is no effect, no difference or no relationship; the alternative hypothesis (H1) states that there is. Statistical tests evaluate how likely the observed data would be if H0 were true.
If the probability (p-value) is below the chosen significance level, typically 0.05, we reject the null hypothesis; otherwise we fail to reject it, which does not prove it is true. Two kinds of errors are possible: a type I error (false positive, rejecting a true null) and a type II error (false negative, missing a real effect). The approach builds on falsification.
Example: H0 says a drug has no effect on blood pressure.
Remember: we never accept the null; we only fail to reject it.
Frequently asked questions
What is Null Hypothesis?
The default assumption of no effect or difference, which data may lead us to reject.
Which area of biology covers Null Hypothesis, and how advanced is it?
Null Hypothesis is one of the 51 topics in Lab Methods & Biostatistics. It is rated Intermediate, so it suits learners who already know the core vocabulary. Related topics in this area include Statistical Power, P-Values and Multiple Testing, t-Test.
Can you give an example of Null Hypothesis?
H0: the drug has no effect on blood pressure.
What should I learn before Null Hypothesis?
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to Null Hypothesis. Intermediate topics assume the Beginner vocabulary.
What topics are related to Null Hypothesis?
The closest topics to Null Hypothesis are Statistical Power, P-Values and Multiple Testing, t-Test, Chi-Squared Test. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand Null Hypothesis?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Null Hypothesis. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
How is Null Hypothesis different from Statistical Power?
Null Hypothesis: The default assumption of no effect or difference, which data may lead us to reject. Statistical Power: The probability of detecting a true effect, determined by sample size, effect size and variability. Read both side by side and note what each one explains.
How should I study Null Hypothesis step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Statistical Power and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.