Central Limit Theorem Interview Guide
Central limit theorem interview guide for sample means, normal approximation, standard error, assumptions, and quant interview caveats.
Candidates who need inference intuition and approximation discipline.
CLT is about averages
The central limit theorem says sample means can become approximately normal under suitable conditions, even when individual observations are not normal.
Sample size matters
Larger samples usually make the approximation better, but dependence, heavy tails, or bad sampling can still cause problems.
Concrete example
Average trade size over many independent observations may be more stable than a single trade size. The mean is the object that gets the approximation.
Connect to standard error
The spread of the sample mean shrinks with sample size. This is why more observations can produce tighter intervals when the sampling assumptions are reasonable.
Common mistakes
Candidates often say CLT makes the data normal. It is the sampling distribution of the mean that is approximated, not necessarily the raw data.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.