Explain Confidence Interval
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Question: How would you explain a confidence interval to a non-technical audience?
A confidence interval is a range of values that’s likely to contain the true value of a population parameter. If we repeatedly sample from the population, about 95% of the confidence intervals calculated from those samples will contain the true mean (assuming a 95% confidence level).
For instance, if you want to estimate the average height of men in the US and your sample gives a mean of 70 inches with a 95% confidence interval of [68, 72], then you can say you’re 95% confident that the true average height lies within that range. A narrower confidence interval indicates greater precision and less uncertainty in the estimate.
A confidence interval gives you a range of values where you can be reasonably sure the true value of something lies. It helps us understand the uncertainty around an estimate we've measured from a sample of data. Typically, confidence intervals are set at the 95% confidence level. For example, A/B test results show that variant B has a CTR of 10.5% and its confidence intervals are [9.8%, 11.2%], this means that based on our sampled data, we are 95% confident that the true avg CTR for variant B among all users (not just the sample) is somewhere between 9.8% and 11.2%.