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| 베이즈 기술 통계× | 효과 크기 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1763/1812 | 1969 (first edition); 1988 (definitive second edition) |
| 창시자≠ | Thomas Bayes / Pierre-Simon Laplace | Jacob Cohen |
| 유형≠ | Bayesian parameter estimation | Standardized magnitude estimation |
| 원전≠ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| 별칭 | Bayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summaries | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis |
| 관련≠ | 5 | 4 |
| 요약≠ | Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation. | 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. |
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