Сравнение методов

	Чувствительность и специфичность	Effect Size
Область	Статистика исследований	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1978	1988
Автор метода	Multiple sources in medical diagnosis and signal detection	Jacob Cohen
Тип	Concept	Concept
Основополагающий источник	Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Другие названия	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic	ES, Cohen's d, standardized effect, practical significance
Связанные	4	4
Сводка	Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity.	Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.