falsepositiivsuse

Falsepositiivsuse is the occurrence of a positive result in a test or analysis when the condition tested for is not actually present. In statistics it corresponds to a Type I error: a true null hypothesis is rejected. The term is used in Estonian as well as other languages to describe this phenomenon.

It appears across fields such as medicine, diagnostics, software security, and environmental monitoring. Examples include a cancer screening test indicating disease when the person is healthy, or a spam filter flagging a legitimate email as phishing.

Measurement and related concepts: the false positive rate (FPR) is the proportion of non-cases wrongly identified as positive. The alpha level in hypothesis testing is the probability of a false positive under the null. Positive predictive value depends on prevalence; with low prevalence, PPV can be low even for accurate tests.

Impact: false positives can cause anxiety, unnecessary procedures, exposure to risks, and wasted resources. They can skew statistics and distract from true signals.

Reduction: adjust thresholds, use confirmatory or sequential testing, control for multiple testing, and adopt Bayesian approaches that incorporate prior information. Considering pre-test probability and local prevalence helps interpret positive results.

Conclusion: falsepositiivsuse is a common issue in diagnostic design and evaluation, requiring careful balance between sensitivity and specificity to minimize harm while maintaining detection of real cases.