I knew the textbook definition cold but the 'layman's terms' part tripped me up.
Start by defining hypothesis testing as a structured way to compare two possibilities using data, then explain p-values as a measure of how surprising the data is if the null hypothesis were true. Use a concrete, everyday analogy (like a coin flip or a clinical trial) to make it intuitive, and tie it back to A/B testing at Google.
Pro tip: Emphasize that a p-value is not the probability that the null hypothesis is true, nor does it measure effect size or practical significance—this shows you understand common misinterpretations and can communicate results responsibly to stakeholders.
Explain that it's a method to decide between two competing claims: a default assumption (null hypothesis) and an alternative claim we want evidence for.
Describe the p-value as the probability of seeing data as extreme as what we observed, assuming the null hypothesis is true. Lower values mean the data is less likely under the null.
Illustrate with a simple example, such as flipping a coin 100 times and getting 90 heads—how surprising that would be if the coin were fair, and how that relates to rejecting the null.
Relate it to product analytics: in an A/B test, the null hypothesis is that the new feature has no effect; a low p-value suggests the observed difference is unlikely due to chance alone.
Mention that p-values don't measure the size or importance of an effect, and that other factors like sample size and practical significance matter.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.