Start with a clear, precise definition of a p-value as the probability of observing data at least as extreme as the observed, assuming the null hypothesis is true. Then connect it to A/B testing by explaining how it's used to decide whether an observed difference is statistically significant, emphasizing that it is not the probability that the null hypothesis is true. Finally, mention common pitfalls and best practices to show depth.
Pro tip: Emphasize that a p-value alone doesn't measure effect size or practical significance; always pair it with confidence intervals and business impact to make better decisions.
State that a p-value is the probability of obtaining a result at least as extreme as the observed data, given that the null hypothesis is true. Avoid saying it's the probability the null is true.
Describe how it's compared to a pre-defined significance level (alpha, often 0.05) to decide whether to reject the null hypothesis. A small p-value suggests the observed effect is unlikely under the null.
In A/B testing, the null hypothesis is typically that there is no difference between control and treatment. A low p-value indicates that the observed difference is statistically significant, but it doesn't guarantee practical importance.
Mention that p-values are often misinterpreted, sensitive to sample size, and don't measure effect size. Recommend using confidence intervals, effect sizes, and considering business context alongside p-values.
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