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| Nullhipotézis-tesztelés× | Konfidencia-intervallum× | |
|---|---|---|
| Tudományterület | Kutatási statisztika | Kutatási statisztika |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1925 | 1937 |
| Megalkotó≠ | Ronald Fisher; Neyman & Pearson | Jerzy Neyman |
| Típus | Concept | Concept |
| Alapmű≠ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ | 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. DOI ↗ |
| Alternatív nevek≠ | NHST, hypothesis formulation, null hypothesis, alternative hypothesis | CI, 95% CI, credible interval, interval estimate |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | 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. |
| ScholarGateAdatkészlet ↗ |
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