Base Rate Fallacy Quant Interview Questions
How to avoid base-rate fallacy mistakes in quant interview Bayes, conditional probability, and signal-quality prompts.
Candidates who confuse likelihoods with posterior probabilities in interview settings.
What the base rate does
The base rate is the prior probability before the new signal. When the base rate is small, false positives can dominate even if the signal sounds accurate.
Convert percentages to counts
Counts make the fallacy visible. A 99 percent accurate test can still produce many false positives if the true condition is extremely rare. Count true positives and false positives separately.
Concrete example
If 0.1 percent of cases are true and a signal falsely flags 1 percent of false cases, then false positives can outnumber true positives. The posterior depends on both signal accuracy and base rate.
Trading and research analogy
In quant research, a rare true signal and many tested false signals create a similar issue. A promising-looking result needs context: prior plausibility, multiple testing, and validation quality.
Sanity check
If the base rate is tiny, a posterior near the signal accuracy is suspicious. Ask how many false positives appear in a large reference population.
Common mistakes
Candidates often quote the test accuracy as the answer. The interview answer should combine prior probability, likelihood, and normalization.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.