Linked from

The 32 pages that link to P-value, each with the reason it gives.

• Sampling distributionRelated: Its calculation depends on the statistic's null sampling distribution.

• Statistical inferenceRelated: Misreading p-values can turn a calibrated test result into an unsupported claim.

• Analysis of varianceRelated: ANOVA reports a p-value for evidence against its null, not the probability that the null is true.

• Statistical significanceRelated: It quantifies the data’s extremeness under the null, not the probability the null is true.

• Multiple comparisons problemRelated: Repeatedly screening p-values creates more chances for a seemingly small value.

• Null hypothesisRelated: It quantifies tail-area evidence against the null, without giving the probability that the null is true.

• Statistical hypothesis testingRelated: It quantifies tail-area compatibility under the null, not the probability that the null is true.

• Quantitative researchRelated: Misinterpretation of p-values can turn statistical evidence into overstated claims.

• Type I and type II errorsRelated: Comparing it with the significance level determines whether the test rejects the null.

• Sample sizeRelated: With sufficiently large samples, even small effects can produce small p-values.

• BiostatisticsRelated: Its interpretation is central to reported evidence and frequently misunderstood in medical studies.

• Student's t-testRelated: The t-distribution converts the test statistic into evidence against the null hypothesis.

• Null hypothesis significance testingRelated: The test compares this tail probability with its preset significance level.

• Chi-squared testRelated: It expresses how unusual the computed statistic is under the null model.