The null hypothesis is the set of parameter values or probability distributions against which a statistical test controls its probability of rejection.
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- Statistical hypothesis test
- Statistical modelling
- Statistical model
- Probability and statistics
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Incoming links (165)
- Alternative hypothesis
- Effect size
- Equivalence test
- Exact conditional Pearson test for a binary contingency table
- False discovery proportion
- Futility boundary
- Kolmogorov-Smirnov test
- Least-favourable null configuration
- Likelihood-ratio test for an exponential support endpoint
- Likelihood-ratio test for a normal variance
- Likelihood-ratio test for equality of beta shape parameters
- Likelihood-ratio test for equality of exponential means
- Mann–Whitney U test
- McNemar's test
- Monte Carlo test
- Noncentral t-distribution
- Nonparametric statistics
- Normal mean confidence interval and hypothesis test duality
- Null distribution
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- Poisson tail discovery probability
- Rejection region
- Scan statistic
- Signed normal Wald statistic
- Size of a statistical test
- Statistical hypothesis
- Statistical inference
- Statistical significance
- Statistical test
- Two-sample Kolmogorov-Smirnov test
- Two-sided hypothesis test
- Type I and type II errors
- Uniformly most powerful test for a gamma rate
- Welch's t-test
- Wilcoxon signed-rank test
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Articles by others on the same topic (1)
The null hypothesis is a fundamental concept in statistics and hypothesis testing. It is a statement that asserts there is no effect or no difference in a given situation, and it serves as a default or starting position for statistical analysis. The null hypothesis is usually denoted as \( H_0 \). For example, in a clinical trial, the null hypothesis might state that a new medication has no effect on patients compared to a placebo.