Quant Interview Statistics Roadmap
A quant interview statistics roadmap covering distributions, variance, covariance, regression intuition, sampling, and research judgment.
Quant researcher, trader, and developer candidates refreshing statistics for interviews.
Start with distribution intuition
Statistics prep should begin with what distributions mean: center, spread, tails, dependence, and sampling variability. Interviewers often care more about whether you can reason from shape and assumptions than whether you can recite a density.
Make variance and covariance practical
Variance and covariance appear in portfolios, signals, experiments, and risk. Practice explaining how variance of a sum changes when variables are correlated. This matters more than memorizing a formula without knowing when the covariance term appears.
Add regression and inference
For research-style interviews, practice interpreting coefficients, residuals, sample size, p-values, confidence intervals, and selection bias. A good answer says what the model suggests and what could make that suggestion unreliable.
Concrete roadmap example
A statistics block might spend two sessions on distributions and moments, two on covariance and correlation, two on regression interpretation, one on hypothesis testing intuition, and one on critiquing a small backtest or experiment.
Connect statistics to evidence
Quant research interviews often ask whether a result is believable. Discuss sample size, leakage, transaction costs, out-of-sample testing, and regime dependence. The goal is not cynicism; it is calibrated evidence.
Common mistakes
Candidates confuse independence with zero correlation, quote tests without assumptions, or treat a backtest as proof. Always say what would make you more or less confident in the result.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.