Microsoft ML Foundations: Statistical Analysis & A/B Tests

This ML foundation question tests your ability to design, run, and interpret statistical analyses that support model decisions and product experiments. You’ll be asked to connect core concepts—p-values, hypothesis tests, confidence intervals, statistical power—to real ML scenarios such as model-accuracy differences, revenue lift, or fairness metrics.

Expect a flow that begins with problem framing (what metric and what business question), moves to experiment design (randomization, unit of analysis, sample-size calculation, primary vs secondary metrics), then to analysis (choose appropriate test, compute p-values and confidence intervals, evaluate power), and ends with interpretation and operational concerns (sample ratio mismatch, multiple comparisons, and robustness checks).

You should demonstrate both theoretical understanding and practical judgment: know how to set null and alternative hypotheses, interpret p-values without overstating evidence, compute and use confidence intervals for effect sizes, and plan experiments to achieve desired power. Show familiarity with A/B test issues—blocking, covariate adjustment, sequential testing, and false-discovery control—and methods for non-normal or heavy-tailed metrics (bootstrapping, transformations, nonparametric tests).

In interviews, illustrate answers with concrete numbers (sample-size formula or a simple power calc), and cite diagnostics you’d run in production to validate results. This question rewards clear reasoning about trade-offs between speed, risk of error, and business impact.