পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| Mixed ANOVA× | একমুখী ভেদাঙ্ক বিশ্লেষণ× | যুগ্ম নমুনা টি-পরীক্ষা× | |
|---|---|---|---|
| ক্ষেত্র | পরিসংখ্যান | পরিসংখ্যান | পরিসংখ্যান |
| পরিবার | Hypothesis test | Hypothesis test | Hypothesis test |
| উদ্ভবের বছর≠ | 1925 | 1925 | 1908 |
| প্রবর্তক≠ | R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation | Ronald A. Fisher | Student (W. S. Gosset) |
| ধরন≠ | Parametric factorial ANOVA | Parametric mean comparison | Parametric mean comparison (paired) |
| মৌলিক উৎস≠ | 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 |
| অপর নাম | 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 |
| সম্পর্কিত≠ | 6 | 4 | 4 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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