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| Analiza ROC (charakterystyka odbiornika)× | Analiza wielkości efektu× | |
|---|---|---|
| Dziedzina | Statystyka | Statystyka |
| Rodzina | Hypothesis test | Hypothesis test |
| Rok powstania≠ | 1954 (signal detection); 1982 (AUC formalization) | 1969 (first edition); 1988 (definitive second edition) |
| Twórca≠ | Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics) | Jacob Cohen |
| Typ≠ | Diagnostic accuracy evaluation | Standardized magnitude estimation |
| Źródło pierwotne≠ | Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. DOI ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 978-0805802832 |
| Inne nazwy | ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis | effect magnitude estimation, standardized effect measure, practical significance analysis, ES analysis |
| Pokrewne | 4 | 4 |
| Podsumowanie≠ | ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination). | 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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