Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Jaudas analīze× | Neatkarīgo paraugu t-tests× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Hypothesis test | Hypothesis test |
| Izcelsmes gads≠ | 1969 (1st ed.); 1988 (seminal 2nd ed.) | 1908 |
| Autors≠ | Jacob Cohen | Student (W. S. Gosset) |
| Tips≠ | Sample size and power planning | Parametric mean comparison |
| Pirmavots≠ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 | Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Citi nosaukumi | sample size calculation, power calculation, sensitivity analysis, a priori power analysis | two-sample t-test, unpaired t-test, Student t-test, independent groups t-test |
| Saistītās≠ | 5 | 4 |
| Kopsavilkums≠ | Power analysis is a planning and evaluation technique that quantifies the probability of detecting a real effect of a given magnitude at a chosen significance level. It links four quantities — sample size, effect size, significance level (alpha), and statistical power (1 minus beta) — so that researchers can determine the sample size needed before data collection or evaluate the sensitivity of a completed study. | The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences. |
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