Multiple Hypothesis Testing Quant Interview Guide
Multiple hypothesis testing quant interview guide covering many tests, false discovery, p-values, corrections, holdouts, and examples.
Candidates testing many signals, parameters, or datasets.
Multiple tests inflate false positives
When many hypotheses are tested, the chance of finding at least one apparently significant result by luck can become large.
P-values need context
A low p-value after one pre-specified test means something different from a low p-value found after many unreported searches.
Concrete example
If a researcher tests one thousand signals, some may pass a five-percent threshold even if all are noise by construction.
Use safeguards
Pre-registration, holdouts, false-discovery controls, economic rationale, robustness checks, and live validation can reduce overclaiming.
Common mistakes
Candidates often present statistical significance without the search count. Interviewers expect a multiple-testing caveat.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.