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In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated.

An estimator or decision rule with zero bias is called unbiased.

Otherwise the estimator is said to be biased.

In statistics, "bias" is an objective property of an estimator, and while not a desired property, it is not pejorative, unlike the ordinary English use of the term "bias".

This article deals with decision-making as analyzed in psychology.

See also Decision theory.

In psychology, decision-making is regarded as the cognitive process resulting in the selection of a belief or a course of action among several alternative possibilities.

Every decision-making process produces a final choice; it may or may not prompt action.

Decision-making is the process of identifying and choosing alternatives based on the values and preferences of the decision-maker.

Random number generation is the generation of a sequence of numbers or symbols that cannot be reasonably predicted better than by a random chance, usually through a random-number generator (RNG).

In statistics, quality assurance, and survey methodology, sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population.

Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population.

The power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true.

It can be equivalently thought of as the probability of accepting the alternative hypothesis (H1) when it is true—that is, the ability of a test to detect an effect, if the effect actually exists.

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