Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Analīze efektu lielumam× | Neatkarīgo paraugu t-tests× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Hypothesis test | Hypothesis test |
| Izcelsmes gads≠ | 1969 (first edition); 1988 (definitive second edition) | 1908 |
| Autors≠ | Jacob Cohen | Student (W. S. Gosset) |
| Tips≠ | Standardized magnitude estimation | 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 | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis | two-sample t-test, unpaired t-test, Student t-test, independent groups t-test |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Effect size analysis quantifies the practical magnitude of a statistical result independently of sample size. Rather than asking only whether a difference or relationship is statistically significant, it asks how large it is, using standardized indices such as Cohen's d, eta-squared, omega-squared, or Pearson's r that allow direct comparison across studies and populations. | 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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