T-statistic

In statistics, the t-statistic is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. It is used in hypothesis testing via Student's t-test. The t-statistic is used in a t-test to determine whether to support or reject the null hypothesis. It is very similar to the z-score but with the difference that t-statistic is used when the sample size is small or the population standard deviation is unknown. For example, the t-statistic is used in estimating the population mean from a sampling distribution of sample means if the population standard deviation is unknown. It is also used along with p-value when running hypothesis tests where the p-value tells us what the odds are of the results to have happened. Let be an estimator of parameter β in some statistical model. Then a t-statistic for this parameter is any quantity of the form where β0 is a non-random, known constant, which may or may not match the actual unknown parameter value β, and is the standard error of the estimator for β. By default, statistical packages report t-statistic with β0 = 0 (these t-statistics are used to test the significance of corresponding regressor). However, when t-statistic is needed to test the hypothesis of the form H0: β = β0, then a non-zero β0 may be used. If is an ordinary least squares estimator in the classical linear regression model (that is, with normally distributed and homoscedastic error terms), and if the true value of the parameter β is equal to β0, then the sampling distribution of the t-statistic is the Student's t-distribution with (n − k) degrees of freedom, where n is the number of observations, and k is the number of regressors (including the intercept). In the majority of models, the estimator is consistent for β and is distributed asymptotically normally. If the true value of the parameter β is equal to β0, and the quantity correctly estimates the asymptotic variance of this estimator, then the t-statistic will asymptotically have the standard normal distribution.