Statistical inference

The Anna Karenina principle describes an endeavor in which a deficiency in any one of a number of factors dooms it to failure.

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.

Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices.

In statistics, a confidence interval (CI) is a type of interval estimate of a population parameter.

In statistics, interval estimation is the use of sample data to calculate an interval of possible (or probable) values of an unknown population parameter, in contrast to point estimation, which is a single number.

In statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data.

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

In statistical inference, specifically predictive inference, a prediction interval is an estimate of an interval in which future observations will fall, with a certain probability, given what has already been observed.

In Bayesian statistics, a credible interval is an interval in the domain of a posterior probability distribution or predictive distribution used for interval estimation.

In statistics, a false coverage rate (FCR) is the average rate of false coverage, i.

In statistics, a confidence region is a multi-dimensional generalization of a confidence interval.

In statistical inference, the concept of a confidence distribution (CD) has often been loosely referred to as a distribution function on the parameter space that can represent confidence intervals of all levels for a parameter of interest.