Quant Interview Statistics Mistakes
Common statistics mistakes in quant interviews, including correlation confusion, sample-size overconfidence, p-value misuse, and weak backtest critique.
Candidates preparing for statistics, research, and data-oriented quant interview rounds.
Correlation confusion
Correlation is not causation, and zero correlation is not general independence. Be precise about whether you mean linear relationship, statistical dependence, or causal effect.
Sample-size overconfidence
Small samples can produce noisy results. Large samples can still be biased. In interviews, discuss sampling error, selection bias, and whether the data-generating process matches the claim.
P-value misuse
A p-value does not prove a hypothesis true. It says something about how surprising data would be under a null model. Explain assumptions and practical significance, not only statistical significance.
Concrete example
A backtest with high returns over few trades may be fragile. A stronger answer asks about leakage, costs, sample size, regime dependence, and whether the result survives out-of-sample testing.
Weak critique
Saying "this might overfit" is a start, not an answer. Add a test: later-period validation, cross-validation where appropriate, higher transaction costs, or a simpler benchmark.
Common mistakes
Candidates often memorize statistical vocabulary without using it to evaluate evidence. Quant interviews reward statistical reasoning tied to the actual data and decision.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.