Statistics
How to describe data, assess model quality, and make decisions from evidence.
descriptive stats
bias-variance
hypothesis testing
p-values
correlation vs causation
Topic overview
Use statistics to summarize data, compare models, reason about variation, and decide whether evidence is strong enough to trust.
Core concepts
Focus on mean vs median, variance, standard deviation, covariance, correlation, bias-variance, confidence intervals, hypothesis tests, p-values, and causality limits.
Why it matters
Statistics keeps analysis honest: it helps you spot noisy results, misleading averages, spurious correlations, overfitting, and weak experiment conclusions.
Interview relevance
Data and ML interviews often test whether you can explain results, choose metrics, interpret experiments, and avoid false confidence.