Salīdzināt metodes

	Statistiskā jauda un izlases lielums	Nulles hipotēzes testēšana
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1988	1925
Autors	Jacob Cohen	Ronald Fisher; Neyman & Pearson
Tips	Concept	Concept
Pirmavots	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Citi nosaukumi	power analysis, sample size calculation, 1 minus beta, sensitivity	NHST, hypothesis formulation, null hypothesis, alternative hypothesis
Saistītās	4	4
Kopsavilkums	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.	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.