Hypothesis testing is a structured way to decide whether an observed difference is bigger than routine noise. You assume nothing is happening, then measure how badly the data contradicts that assumption.
The walk-through I use:
State the null and the alternative before looking at data. Null: the new checkout converts at the same rate as the old one.
Pick the significance level and a test that matches the metric and the design.
Compute the test statistic and its p-value from the collected sample.
Compare against the threshold, then report the effect size and interval next to the verdict.
Order matters more than the arithmetic. Choosing the metric or the threshold after seeing results turns the procedure into storytelling. The most common production failure is not a wrong formula. It is a null hypothesis that got quietly rewritten once the numbers landed.
Rewriting in plainer words…
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