On the problem of the most efficient tests of statistical hypotheses.¶
Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London, Series A, 231(694–706), 289-337.
Cited by¶
6 citations across 6 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Bayesian Updating
- T3 — Bayesian philosophy vs regulatory and publication norms. Many regulatory frameworks (FDA, EMA for drug approval; academic journal cultures in psychology, epidemiology) have traditionally been built around frequentist hypothesis testing with pre-specified error rates
This sourceFoundational paper of frequentist hypothesis testing with pre-specified error rates (Type I/II), the framework around which regulatory and publication norms were historically built. SUPPORTS marker 014 (re-sourced from efron-1979, whose bootstrap paper does not establish that regulators use frequentist error rates).
- T3 — Bayesian philosophy vs regulatory and publication norms. Many regulatory frameworks (FDA, EMA for drug approval; academic journal cultures in psychology, epidemiology) have traditionally been built around frequentist hypothesis testing with pre-specified error rates
- Hypothesis Testing (Null vs. Alternative)
- Supporting structural elements include: pre-specified significance level α (conventionally 0.05, 0.01, or domain-specific); one-sided versus two-sided alternative hypotheses (directional or either-direction); parametric versus nonparametric test choices (distributional assumptions); exact versus asymptotic p-values (Fisher exact test or normal approximation); and randomization-based versus model-based inference (permutation test or parametric model)
This sourceFoundational paper: frames inferential conclusions as tentative decisions with controlled long-run error rates, subject to revision as new data accumulate.
- Supporting structural elements include: pre-specified significance level α (conventionally 0.05, 0.01, or domain-specific); one-sided versus two-sided alternative hypotheses (directional or either-direction); parametric versus nonparametric test choices (distributional assumptions); exact versus asymptotic p-values (Fisher exact test or normal approximation); and randomization-based versus model-based inference (permutation test or parametric model)
- Statistical Inference
- As Neyman and Pearson (1933) framed it, a conclusion from inference is tentative, assigned a probability or confidence level, and subject to revision as new data arrives.
This sourceFoundational paper: frames inferential conclusions as tentative decisions with controlled long-run error rates, subject to revision as new data accumulate.
- As Neyman and Pearson (1933) framed it, a conclusion from inference is tentative, assigned a probability or confidence level, and subject to revision as new data arrives.
- Statistical Power
- Formally, power = P(reject H₀ | H₁ is true), equivalently 1−β where β is the Type II error probability
This sourceFoundational paper: frames inferential conclusions as tentative decisions with controlled long-run error rates, subject to revision as new data accumulate.
- Formally, power = P(reject H₀ | H₁ is true), equivalently 1−β where β is the Type II error probability
- Statistical Significance (p-Value)
- These are conceptually distinct even when the p-value is compared to α for decision-making
This sourceFoundational paper: frames inferential conclusions as tentative decisions with controlled long-run error rates, subject to revision as new data accumulate.
- These are conceptually distinct even when the p-value is compared to α for decision-making
- Type I & Type II Errors
- A Type I error (false positive, α-error) wrongly rejects a true null hypothesis—declaring an effect that does not exist—while a Type II error (false negative, β-error) wrongly retains a false null hypothesis—failing to detect an effect that does exist
This sourceFoundational paper: frames inferential conclusions as tentative decisions with controlled long-run error rates, subject to revision as new data accumulate.
Supported in partVerified against the publisher's abstract
The work's title names the 'most efficient tests' program the claim describes, but only the 1933 abstract was retrieved, and it surveys pre-1933 history rather than the alpha/power rule.
“IX. On the problem of the most efficient tests of statistical hypotheses”
- A Type I error (false positive, α-error) wrongly rejects a true null hypothesis—declaring an effect that does not exist—while a Type II error (false negative, β-error) wrongly retains a false null hypothesis—failing to detect an effect that does exist
Verification¶
Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.
Does it back the claim? Read against the text for 1 of 6 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
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