השוואת שיטות
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| ANOVA משולבת× | ניתוח שונות חד-כיווני× | מבחן t לדגימות מזווגות× | |
|---|---|---|---|
| תחום | סטטיסטיקה | סטטיסטיקה | סטטיסטיקה |
| משפחה | Hypothesis test | Hypothesis test | Hypothesis test |
| שנת המקור≠ | 1925 | 1925 | 1908 |
| הוגה השיטה≠ | R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation | Ronald A. Fisher | Student (W. S. Gosset) |
| סוג≠ | Parametric factorial ANOVA | Parametric mean comparison | Parametric mean comparison (paired) |
| מקור מכונן≠ | Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE. ISBN: 978-1526419521 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE. ISBN: 978-1446249185 |
| כינויים | split-plot ANOVA, mixed-design ANOVA, between-within ANOVA, Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı) | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA | dependent samples t-test, repeated measures t-test, matched-pairs t-test, eşleştirilmiş örneklem t-testi |
| קשורות≠ | 6 | 4 | 4 |
| תקציר≠ | Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. | The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject difference scores rather than the raw measurements. |
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