Law of Large Numbers Interview Questions
Law of large numbers interview prep for sample averages, long-run frequencies, coin-flip intuition, CLT contrast, and gambler-fallacy mistakes.
Candidates preparing for probability intuition and statistics foundations.
Long-run averages stabilize
The law of large numbers says that as you repeat a stable experiment many times, the sample average tends to move toward the true expected value.
Coin-flip intuition
For fair coin flips, the fraction of heads tends to settle near 1/2 over many flips. That does not mean every short sequence must look balanced.
Contrast with CLT
The law of large numbers explains convergence of averages. The central limit theorem describes the approximate shape of fluctuations around the mean.
Interview use
Use this idea when explaining why repeated sampling, repeated trials, or averages become more stable with larger samples.
Short-run caution
The law does not say a long run of tails makes heads due. Independent trials do not compensate for past imbalance in that way.
Common mistakes
Candidates often turn long-run stabilization into short-run prediction. Keep the statement about averages over many trials.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.