Point estimation

Summary

Point estimation is the part of statistical inference that computes from data a single best guess of an unknown quantity. A typical unknown quantity is a parameter of a statistical model. The rule that turns data into the guess is an estimator, and its value on the observed data is the estimate. The theory compares estimators by bias, variance, mean squared error, consistency and efficiency. It also gives general methods to build them, such as maximum likelihood and the method of moments. It is contrasted with interval estimation, which gives a range of plausible values.