Your team ships a one-page checkout to half the store's visitors. 2 weeks later the dashboard says treatment converts 10.3% and control 10.0%, and the interviewer asks the obvious question: is that 0.3-point gap real, or would two identical checkouts have drifted that far apart by luck? Hypothesis testing is the tool that answers it. You measure the gap in units of its own noise, turn that into a p-value, and report a confidence interval, the range of true effects the data still allows.

This is the analysis step of almost every experimentation round, after the design and before the launch call, and it usually gets five to ten minutes. For a mid-level role, the bar is computing the standard error, the test statistic, and the interval, then reading the p-value without the classic mistakes. For a senior role, interviewers also listen for whether you judge the result against a business bar and know when the bell-curve shortcut behind these formulas (the normal approximation) stops being trustworthy, as it does for revenue with a few huge baskets.

The tutorial assumes the test is already randomized and running, the setup covered in Experiment Design, and it stays with one planned comparison of one metric. Choosing the sample size and handling repeated looks at the data are separate sections of this course.