False Positive, within the realm of statistical analysis and hypothesis testing, arises when a test erroneously rejects the null hypothesis. This implies that the test suggests a statistically significant effect or distinction, whereas, in actuality, none exists.

For instance, consider conducting an A/B test on a website to ascertain whether a new design (Variant B) yields more clicks on a specific button compared to the current design (Variant A). The null hypothesis posits that no difference in click count exists between the two designs.

In a scenario where the test outcome indicates that Variant B significantly garners more clicks, you would reject the null hypothesis and infer the new design's superiority. However, if this outcome constitutes a false positive, it signifies the test inaccurately indicated a disparity. In reality, the new design doesn't generate more clicks, and the observed difference resulted from random chance or another confounding factor.

False positives are likelier to occur during multiple comparisons (testing multiple hypotheses simultaneously) or when the test is conducted multiple times. This predicament is termed the multiple comparisons problem or the problem of multiple testing.

To manage the false positive rate, diverse correction methods are deployed, including the Bonferroni correction and the Benjamini-Hochberg procedure. These techniques adapt the significance level (the threshold for null hypothesis rejection) based on the number of comparisons made.

It's pertinent to note that a test's significance level (typically set at 0.05 or 5%) signifies the likelihood of encountering a false positive. Therefore, even if no genuine effect or difference exists, an approximately 5% false positive result can be expected purely by chance.

In the context of Statsig, false positives can be mitigated via approaches such as the Bonferroni correction, Sequential Testing, and CUPED. These methodologies aid in modifying calculated p-values and confidence intervals, diminishing false positive rates—especially when evaluating outcomes prior to the experiment's target completion date.