Make the experiment defensible
A research answer connects an estimand, a dataset, a validation design and a decision. Calculating a coefficient matters; explaining why it is identifiable and honestly evaluated matters more than quoting a formula.
Two Sigma’s guidance includes open-ended analysis, coding, algorithms and statistics or research-domain discussion. Citadel’s campus guidance also emphasizes programming and explaining trade-offs.
Worked example: effective information
For 240 observations with AR(1) correlation 0.5, the large-sample approximation n_eff = n(1−ρ)/(1+ρ) gives 80. This is an approximation under a specified dependence structure, not permission to substitute 80 in every statistical test. Overlapping labels or regime changes may invalidate the model.
Preparation order
- Probability, expectation, variance and conditioning.
- Inference, regression, matrix operations and estimator uncertainty.
- Leakage, multiple testing, time-aware splits and costs.
- Algorithms, numerical stability and clear research exposition.
Bring one real project
Explain its baseline, failed experiments, validation choices, limitations and next experiment. Implement analysis outside QuantPrep too: numeric drills and self-evaluated prompts do not provide a dataset workspace or executable coding sandbox.
Try a fresh question
This optional exercise uses the actual parameterized bank.
Worked method
Keep the practice connected
Train Data & Modeling · Follow the Quant Path · Evidence standard