Reading the evidence
A false positive occurs when a test signals an effect or condition that is not present under the relevant truth definition. The rate depends on thresholds, prevalence and the testing process.
Make it click
A sensitive alarm may occasionally ring because of steam. The alarm event is real, while the condition it is meant to indicate is absent.
Now put that into markets
A market screen can flag chance variation as a pattern. Testing many candidates creates many opportunities for at least one false alarm.
Type I error is controlled under a specified null and procedure, not across an undisclosed search. Positive predictive value also depends on base rates.
Count all tests, define truth and report the decision threshold. Use confirmation on fresh evidence rather than recycling discovery data.
Reducing false positives can increase false negatives. No threshold avoids both costs, so context and consequences matter.
Takeaway
