In inferential statistics, a confidence interval (CI) is a type of interval estimate, computed from the statistics of the observed data, that might contain the true value of an unknown population parameter. The interval has an associated confidence level that, loosely speaking, quantifies the level of confidence that the parameter lies in the interval. More strictly speaking, the confidence level represents the frequency (i.e. the proportion) of possible confidence intervals that contain the true value of the unknown population parameter. In other words, if confidence intervals are constructed using a given confidence level from an infinite number of independent sample statistics, the proportion of those intervals that contain the true value of the parameter will be equal to the confidence level. Confidence intervals were introduced to statistics by Jerzy Neyman in a paper published in 1937.
- Neyman, J. (1937). “Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability”. Philosophical Transactions of the Royal Society A. 236 (767): 333–380.