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The default claim that there is no effect, difference, or association, against which statistical tests seek evidence.
DESCRIPTION
The null hypothesis (H0) is a baseline statement, such as no difference between groups or no association between variables. Statistical tests compute test statistics and p-values to assess compatibility of data with H0. Rejection of H0 suggests evidence for an alternative hypothesis (H1). Proper interpretation avoids equating non-rejection with proof of no effect. Significance levels control Type I error rates, while power analyses address Type II error. Formulating clear, testable H0 is foundational to experiment design, A/B tests, and scientific inference.
ALTERNATIVE DEFINITIONS
Wikipedia
In statistics, the null hypothesis is a general statement or default position that there is no relationship between two measured phenomena, or no association among groups.
Frames H0 as a general default of no relationship or difference.
NIST/SEMATECH e-Handbook
The null hypothesis, H0, is a statement of no effect or no difference and is assumed true until evidence indicates otherwise.
Emphasizes assumption of truth until evidence contradicts.
RELATED TERMS
p-value Type I Error Type II Error Significance Level Test Statistic Power (Statistics) t-test Confidence Interval Effect Size
COMPETING TERMS
RELATIONS
- Broader: Hypothesis
KNOWLEDGE AREAS
Mathematics Data Science Testing & Validation
TARGET AUDIENCE
Data Scientist Data Analyst Information Analyst AI Engineer
EXTERNAL SOURCES
- Wikipedia - Null hypothesis
- Statistics by Jim - Accessible explanation of H0
- Statistics How To - Guide to null hypothesis basics
- Khan Academy - Significance testing course
TOP QUESTIONS
Why is the Null Hypothesis essential in A/B testing?
It establishes a default of no difference, enabling objective significance testing of experimental changes.
Category: Relevancy
Does failing to reject H0 prove no effect?
No. It indicates insufficient evidence; low power or small samples can mask real effects.
Category: Misunderstanding
How do Type I and Type II errors relate to H0?
Type I rejects a true H0; Type II fails to reject a false H0. Significance and power balance them.
Category: Basics
What is a common mistake with Null Hypothesis tests?
P-hacking or multiple testing without correction inflates false positive rates.
Category: Mistakes
How should H0 be formulated?
Clearly define measurable parameters and specify equality or no-difference claims.
Category: Requirements
What is the role of effect size in H0 testing?
Effect sizes quantify practical importance beyond statistical significance.
Category: Concepts
How does sample size affect H0 tests?
Larger samples increase power, enabling detection of smaller effects.
Category: Mechanism
What alternatives exist to Null Hypothesis testing?
Bayesian inference, estimation-focused analysis, and equivalence tests provide complementary perspectives.
Category: Alternatives
How to control false discoveries with many tests?
Use corrections such as Bonferroni or FDR (Benjamini–Hochberg).
Category: Best practices