Start by defining the confidence level in simple terms: it's the long-run frequency that the interval captures the true parameter if we repeated the experiment many times. Then, emphasize that it's not the probability that the parameter lies in the interval, and connect it to practical decision-making in A/B testing.
Pro tip: Use a concrete example, like running 100 A/B tests with 95% confidence intervals, and note that about 95 of them will contain the true effect. This shows you understand the frequentist interpretation and can communicate it clearly.
Explain that the confidence level (e.g., 95%) represents the proportion of intervals that would contain the true parameter if the same study were repeated many times.
State that it is not the probability that the true parameter falls within a specific interval; the parameter is fixed, and the interval is random.
Discuss how confidence level relates to false positive rate (1 - confidence level) and its role in determining statistical significance in experiments.
Mention that higher confidence levels produce wider intervals, requiring larger sample sizes to detect meaningful effects, which impacts product decisions.
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