The tricky part isn't knowing what a p-value is, it's explaining it without sounding like a textbook.
Start with a plain-language definition of a p-value as the probability of seeing results at least as extreme as observed, assuming no real effect. Then explain its role in A/B testing: it helps quantify evidence against the null hypothesis, but it is not the probability that the null is true or that the result is practically important. Finally, connect it to product decisions by emphasizing that p-values must be combined with effect size, business impact, and context.
Pro tip: Emphasize that a p-value alone should never drive a product decision; always pair it with confidence intervals and practical significance to avoid overreacting to statistically significant but trivial effects.
Explain that a p-value is the probability of observing data as extreme as yours if there were truly no effect (null hypothesis true). Avoid jargon like 'type I error' unless asked.
State that it is not the probability the null is true, nor the probability the result is due to chance, nor a measure of effect size or business importance.
Describe how p-values are used in experiments to decide if a difference between control and treatment is statistically significant, typically with a threshold like 0.05.
Explain that a low p-value suggests the effect is unlikely due to random noise, but product decisions should also consider effect size, confidence intervals, cost, and strategic fit.
Provide a brief example, e.g., a 0.1% lift with p=0.01 may be statistically significant but not worth implementing if the cost outweighs the benefit.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.