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| Sensitivität und Spezifität× | P-Wert und statistische Signifikanz× | |
|---|---|---|
| Fachgebiet | Forschungsstatistik | Forschungsstatistik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1978 | 1925 |
| Urheber≠ | Multiple sources in medical diagnosis and signal detection | Ronald Fisher |
| Typ | Concept | Concept |
| Wegweisende Quelle≠ | Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552. link ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link ↗ |
| Aliasnamen | diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic | p-value, significance test, statistical significance, alpha level |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. | The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05). |
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