Salīdzināt metodes

	Nulles hipotēzes testēšana	Statistiskā jauda un izlases lielums
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1925	1988
Autors	Ronald Fisher; Neyman & Pearson	Jacob Cohen
Tips	Concept	Concept
Pirmavots	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
Citi nosaukumi	NHST, hypothesis formulation, null hypothesis, alternative hypothesis	power analysis, sample size calculation, 1 minus beta, sensitivity
Saistītās	4	4
Kopsavilkums	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.