Σύγκριση μεθόδων

	Ευαισθησία και Ειδικότητα	Μέγεθος Επίδρασης
Πεδίο	Ερευνητική Στατιστική	Ερευνητική Στατιστική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1978	1988
Δημιουργός	Multiple sources in medical diagnosis and signal detection	Jacob Cohen
Τύπος	Concept	Concept
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic	ES, Cohen's d, standardized effect, practical significance
Συναφείς	4	4
Σύνοψη	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.