Salīdzināt metodes

	Jutīgums un specifiskums	Nulles hipotēzes testēšana
Nozare	Pētniecības statistika	Pētniecības statistika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1978	1925
Autors	Multiple sources in medical diagnosis and signal detection	Ronald Fisher; Neyman & Pearson
Tips	Concept	Concept
Pirmavots	Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Citi nosaukumi	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic	NHST, hypothesis formulation, null hypothesis, alternative hypothesis
Saistītās	4	4
Kopsavilkums	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.	Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.