Python Probability Simulation Interview Guide
Python probability simulation interview guide for random draws, trial loops, Monte Carlo estimators, convergence, validation, and examples.
Candidates practicing Monte Carlo and sanity checks.
Simulation estimates a defined quantity
Before coding, define what probability or expected value the simulation estimates. The code should mirror the random experiment exactly.
Use trials and estimators clearly
A basic Monte Carlo structure repeats independent trials, records an outcome, and averages the outcome. Keep the estimator simple and inspectable.
Concrete example
To estimate a hitting probability, simulate paths until they hit one boundary, record success or failure, then average the success indicator.
Check convergence and sanity
Run enough trials for a stable estimate, compare with simple cases, and watch for off-by-one or stopping-condition errors.
Common mistakes
Candidates often simulate without validating the setup. A simulation is useful only if the random experiment and estimator match the prompt.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.