विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मिश्रित एनोवा (Mixed ANOVA)× | एक-तरफ़ा विचरण विश्लेषण× | युग्मित नमूना टी-परीक्षण× | |
|---|---|---|---|
| क्षेत्र | सांख्यिकी | सांख्यिकी | सांख्यिकी |
| परिवार | Hypothesis test | Hypothesis test | Hypothesis test |
| उद्भव वर्ष≠ | 1925 | 1925 | 1908 |
| प्रवर्तक≠ | R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation | Ronald A. Fisher | Student (W. S. Gosset) |
| प्रकार≠ | Parametric factorial ANOVA | Parametric mean comparison | Parametric mean comparison (paired) |
| मौलिक स्रोत≠ | 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 |
| उपनाम | 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 |
| संबंधित≠ | 6 | 4 | 4 |
| सारांश≠ | 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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