Explore: Confidence Interval

In statistics, a confidence interval (CI) is a range of values used to estimate an unknown statistical parameter, such as a population mean. Rather than

In statistics, a binomial proportion confidence interval is a confidence interval for the probability of success calculated from the outcome of a series

called credible set or credible region. Credible intervals are a Bayesian analog to confidence intervals in frequentist statistics. The two concepts arise

can be described by the confidence interval or CI. To show how a larger sample will make the confidence interval narrower, consider the following examples:

The most prevalent forms of interval estimation are confidence intervals (a frequentist method) and credible intervals (a Bayesian method). Less common

A tolerance interval (TI) is a statistical interval within which, with some confidence level, a specified sampled proportion of a population falls. "More

and Gao, S. (1997), "Confidence intervals for the log-normal mean," Statistics in Medicine, 16, 783–790. Confidence Intervals for Risk Ratios and Odds

prediction interval bears the same relationship to a future observation that a frequentist confidence interval or Bayesian credible interval bears to an

while 95% of the confidence intervals will contain the true value of N, this is not the same as N being contained in the confidence interval with 95% probability

data. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. This technique allows