Скачать или смотреть Top 15 Statistics & Probability Interview Questions for Data Science (2026)

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Timestamps
0:00 - Introduction: Why Statistics Matters for Data Science
0:43 - Q1: Bayesian Inference & The Monty Hall Paradox
2:05 - Q2: Poisson vs. Binomial Distribution Dilemma
3:18 - Q3: Central Limit Theorem (CLT) & Statistical Robustness
4:28 - Q4: Stratified Sampling vs. Variance Reduction
5:33 - Q5: Law of Large Numbers vs. Gambler's Fallacy
6:27 - Q6: P-values & Null Hypothesis (NHST) Framework
7:24 - Q7: Type I and Type II Errors (Trade-off of Power)
8:22 - Q8: Confidence Interval vs. Prediction Interval
9:16 - Q9: How to Determine Sample Size for A/B Testing
10:06 - Q10: Parametric vs. Non-parametric Testing
10:51 - Q11: Bias-Variance Trade-off & Model Complexity
11:38 - Q12: L1 vs. L2 Regularization (Lasso & Ridge)
12:34 - Q13: Simpson's Paradox & Confounding Variables
13:28 - Q14: Berkson's Paradox & Selection Bias
14:25 - Q15: Imputation Theory (MCAR, MAR, MNAR)
15:32 - Final Summary: Validating Logic in 2025
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