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False positive


A false positive occurs when a test incorrectly indicates the presence of a condition or characteristic that is not actually present. In the context of statistics and data analysis, a false positive is an error in data reporting in which a test result improperly identifies the presence of a condition (such as a malware) when in reality, it is absent.


Understanding and managing false positives is crucial because they can lead to unnecessary concern, additional testing, wasted resources, and incorrect data-driven decisions. In medical testing, a false positive result might lead a patient to undergo further, potentially invasive, investigations that are not needed. In cybersecurity, false positives in threat detection systems can cause unnecessary alarms and divert attention from real threats.

Key Considerations

Strategies to Reduce False Positives

Benefits of Managing False Positives


False positives represent a significant challenge in various fields, impacting decision-making processes and resource allocation. Effective management of false positives involves improving test specificity, validating results with additional testing, and regularly updating detection methodologies. By minimizing false positives, organizations and individuals can make more accurate and confident decisions, ensuring that resources are used efficiently and effectively.

False positive

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