The ASA’s Statement on p-Values¶
American Statistical Association. (2016). The ASA’s Statement on p-Values: Context, Process, and Purpose. The American Statistician.
Cited by¶
7 citations across 7 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- False Dilemma
- Surfacing the residual — effect-size estimation, confidence intervals, equivalence testing, Bayesian posterior over \(\delta\)
This sourceOfficial ASA statement warning against dichotomous accept/reject inference and recommending estimation and fuller reporting, supporting the surfacing of estimation/equivalence/posterior alternatives to the significance binary.
- Surfacing the residual — effect-size estimation, confidence intervals, equivalence testing, Bayesian posterior over \(\delta\)
- Hypothesis Testing (Null vs. Alternative)
- However, widespread limitations persist: the p-value is routinely misunderstood as the probability that H₀ is true (rather than the probability of data at least as extreme under H₀)
This sourceASA p-value statement clarifying replication implications of significance testing.
- However, widespread limitations persist: the p-value is routinely misunderstood as the probability that H₀ is true (rather than the probability of data at least as extreme under H₀)
- Reproducibility & Replicability
This sourceASA p-value statement clarifying replication implications of significance testing.
- Statistical Significance (p-Value)
- The statement articulates six principles: (1) P-values can indicate how incompatible the data are with a specified statistical model—but the p-value alone does not establish truth of a model
This sourceASA p-value statement clarifying replication implications of significance testing.
- The statement articulates six principles: (1) P-values can indicate how incompatible the data are with a specified statistical model—but the p-value alone does not establish truth of a model
Domain-specific¶
- Statistical Literacy
- A person may calculate an average correctly yet fail to notice a biased sample; recognize a percentage yet overlook its denominator; or quote a p-value yet misstate it as the probability that a hypothesis is true
This sourceThe ASA statement's second principle states directly that 'P-values do not measure the probability that the studied hypothesis is true', which is the misreading named here.
SupportedVerified against the source
- A person may calculate an average correctly yet fail to notice a biased sample; recognize a percentage yet overlook its denominator; or quote a p-value yet misstate it as the probability that a hypothesis is true
- Student's t-Test
Mechanisms¶
- Null Hypothesis Significance Test
- Its failure mode is the one the whole discipline warns about: the p-value gets treated as the answer instead of one conditional probability, and a threshold-crossing becomes a proof of importance, causality, or truth it cannot support.
This sourceDefines the p-value as a probability calculated under a specified statistical model, not the probability that a hypothesis is true or a complete evidentiary answer.
- Its failure mode is the one the whole discipline warns about: the p-value gets treated as the answer instead of one conditional probability, and a threshold-crossing becomes a proof of importance, causality, or truth it cannot support.
Verification¶
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