John Cook takes us through the notion of confidence intervals and credible intervals:
Suppose you do N trials of something that can succeed or fail. After your experiment you want to present a point estimate and a confidence interval. Or if you’re a Bayesian, you want to present a posterior mean and a credible interval. The numerical results hardly differ, though the two interpretations differ.
If you got half successes, you will report a confidence interval centered around 0.5. The more unbalanced your results were, the smaller your confidence interval will be. That is, the confidence interval will be smallest if you had no successes and widest if you had half successes.
What can we say about how the width of your confidence varies as a function of your point estimate p?
Read on to learn that answer.