Correlation vs Independence in Quant Interviews
How to distinguish correlation, zero correlation, and independence in quant interview statistics questions.
Candidates who need clearer dependence language in statistics and probability interviews.
Correlation measures linear relationship
Correlation is a normalized measure of linear co-movement. It is useful because it has no units and falls between -1 and 1, but it does not capture every kind of dependence.
Independence is stronger
Independence means knowing one variable gives no information about the other. If two variables are independent, many forms of dependence disappear. Zero correlation is weaker and only says there is no linear relationship.
The zero-correlation trap
Two variables can be uncorrelated but dependent. For example, if X is symmetric around zero and Y = X squared, the linear correlation can be zero even though Y is fully determined by X.
Concrete interview example
If a signal has zero correlation with returns in one sample, that does not prove it is independent of returns or useless in every setting. It may be nonlinear, regime-dependent, noisy, or simply overfit elsewhere. Explain what evidence you would check next.
How to answer cleanly
Say exactly which claim you are making: independent, uncorrelated, positively correlated, negatively correlated, or causally related. Do not slide between those words. In quant interviews, loose dependence language creates avoidable errors.
Common mistakes
Candidates often say "not correlated" when they mean independent, or treat correlation as causation. A stronger answer states the relationship, the evidence, and what remains uncertain.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.