Amir H. Ghaseminejad shows the meaning of Confidence Interval with a simple example. There are two schools in statistics. In the frequentist school, The parameters of population are unknown constants, 90% confidence interval means that with a large number of repeated samples, 90% of the calculated confidence intervals would include the true value of the mean. In the Bayesian school, a 90% credible interval for the parameter means that the posterior probability that the parameter lies in the interval is 0.9.
An alternative terminology is to use "Bayesian confidence interval" instead of "credible interval".
Many professional statisticians and decisions scientists as well as non-statisticians intuitively interpret confidence intervals in the Bayesian credible interval sense and hence "credible intervals" are sometimes called "confidence intervals". It is widely accepted, especially in the decision sciences, that "credible interval" is merely the subjective subset of "confidence intervals". In fact, much research in calibrated probability assessments never uses the term "credible interval" and it is common to simply use "confidence interval".