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.
- 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¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:7385918b1a8d · see in the full table