Linked from

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

• Confidence intervalCompared with: A p-value tests a hypothesis; it is not the probability that a parameter lies within an interval.

• Statistical powerCompared with: A p-value summarizes observed data under the null; power is a prospective rejection probability under alternatives.

• Ronald FisherCompared with: Fisher treated the p value as a graded measure of evidence, unlike fixed-threshold testing.

• Effect sizeCompared with: Unlike an effect size, it does not directly quantify magnitude.

• Bayesian statisticsCompared with: Its frequentist interpretation differs from the posterior probability statements Bayesian analyses permit.

• Likelihood ratioCompared with: A p-value measures tail-area extremity, not the relative probability of evidence under two hypotheses.

• Bayes factorCompared with: A p-value is not the probability of a model or the ratio of two models’ evidence.

• Posterior probabilityCompared with: A p-value is not the probability that a hypothesis is true after seeing data.

• Egon PearsonCompared with: Fisher treated the p-value as a measure of evidence, unlike a fixed decision rule.

• Jerzy NeymanCompared with: A p-value is not the same as Neyman’s prechosen long-run decision rule.

• Credible intervalCompared with: It is a tail probability under a null model, not posterior probability assigned to parameter values.

• Eyeball theoremCompared with: It quantifies evidence against a null model rather than relying on apparent separation.