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Statistics & Probability Foundations
Master the statistical concepts that underpin every data science and ML interview — from distributions and hypothesis testing to A/B testing and causal inference.
Why Statistics Matters in ML/DS Interviews
Statistics appears in 60-80% of data science and ML interviews. It's not just academic — it's the foundation of how you validate models, design experiments, interpret results, and communicate findings to stakeholders. Companies like Meta, Google, Airbnb, and Stripe specifically test statistical reasoning as a core competency.
The Six Core Areas
Probability Theory
Distributions, conditional probability, Bayes' theorem, expected value, variance
Statistical Inference
Hypothesis testing, p-values, confidence intervals, Type I/II errors, power
A/B Testing
Experiment design, sample size, randomization, multiple comparisons, stopping rules
Bayesian Statistics
Prior/posterior, conjugate models, credible intervals, Bayesian A/B testing
Regression & Correlation
Linear regression assumptions, correlation vs causation, OLS, logistic regression
Causal Inference
DiD, RDD, IV, PSM — methods for estimating causality from observational data
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