Selection Bias Quant Interview Guide
Selection bias quant interview guide for nonrepresentative samples, backtest examples, survivorship, data quality, and mitigation.
Candidates discussing samples, backtests, and data quality.
Selection bias changes the sample
Selection bias appears when the observed sample is not representative of the population or process you want to understand.
Backtests can select winners
A dataset or strategy set chosen after seeing outcomes can overstate performance because poor cases were excluded before the evaluation.
Concrete example
Studying only surviving companies can make historical returns look better than they were for the full investable universe.
Mitigate with design
Use representative sampling, pre-defined inclusion rules, and out-of-sample checks when possible. Name remaining limitations.
Common mistakes
Candidates often assume more data solves bias. More biased data can make a biased estimate more confidently wrong and harder to challenge.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.