Salīdzināt metodes

	Nulles hipotēzes testēšana	Ticamības intervāls
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1925	1937
Autors	Ronald Fisher; Neyman & Pearson	Jerzy Neyman
Tips	Concept	Concept
Pirmavots	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.	Neyman, J. (1937). Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability. Philosophical Transactions of the Royal Society, 236, 333–380.
Citi nosaukumi	NHST, hypothesis formulation, null hypothesis, alternative hypothesis	CI, 95% CI, credible interval, interval estimate
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.	A confidence interval (CI) is a range of values, calculated from sample data, that likely contains the true population parameter. Introduced by Jerzy Neyman in 1937, it provides an interval estimate rather than a single point estimate, incorporating both the observed value and the uncertainty around it. The standard 95% confidence interval is a robust, intuitive alternative to p-values for communicating research results.