Essential Statistics for Data Science · Part 4 of 4
The capstone of the series. Inference is where statistics earns its keep in industry: it's how you go from "we saw a 4% lift" to "we're confident the lift is real, here's the effect size, here's the uncertainty, here's the call." This part covers hypothesis testing (t-tests, chi-square, ANOVA), confidence intervals, p-values, and the assumptions that make or break each one.
It then bridges into regression, simple and multiple linear regression as both a modeling tool and an inferential one, with a clear distinction between prediction and explanation, and the diagnostics that tell you which one your model is actually doing.