Sammenlign metoder

	Hypotesetestning	Statistisk styrke og stikprøvestørrelse
Fagområde	Forskningsstatistik	Forskningsstatistik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1925	1988
Ophavsperson	Ronald Fisher; Neyman & Pearson	Jacob Cohen
Type	Concept	Concept
Oprindelig kilde	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Aliasser	NHST, hypothesis formulation, null hypothesis, alternative hypothesis	power analysis, sample size calculation, 1 minus beta, sensitivity
Relaterede	4	4
Resumé	Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.	Statistical power is the probability of detecting a true effect if it exists (1 − β). Power analysis determines the sample size required to detect a hypothesized effect size with specified Type I error (α) and Type II error (β) rates. Introduced by Jacob Cohen (1988), power analysis is foundational to research design: underpowered studies produce inflated effect size estimates and are unlikely to replicate. The standard benchmark is 80% power (β = 0.20), though critical studies may require 90% power.