Confidence Intervals Explained Simply for Data Scientists

Recently, I got asked about how to explain confidence intervals in simple terms to a layperson. I found that it is hard to do that.

Confidence Intervals are always a headache to explain even to someone who knows about them, let alone someone who doesn’t understand statistics.

I went to Wikipedia to find something and here is the definition:

In statistics , a confidence interval (CI) is a type of estimate computed from the statistics of the observed data. This proposes a range of plausible values for an unknown parameter . The interval has an associated confidence level that the true parameter is in the proposed range. This is more clearly stated as: the confidence level represents the probability that the unknown parameter lies in the stated interval. The level of confidence can be chosen by the investigator. In general terms, a confidence interval for an unknown parameter is based on sampling the distribution of a corresponding estimator . [1]

And my first thought was that might be they …