t-statistic

In <a href="/facts/Statistics/DNPsKGYU">statistics</a>, the t-statistic is the ratio of the difference in a number’s estimated value from its assumed value to its <a href="/facts/Standard_error_(statistics)/Iv96AyCD">standard error</a>. It is used in <a href="/facts/Statistical_hypothesis_testing/yv9RPF6U">hypothesis testing</a> via <a href="/facts/Student%27s_t-test/dMH8uC28">Student's t-test</a>. 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 <a href="/facts/Standard_score/lF8v0Mcy">z-score</a> 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 <a href="/facts/Population_mean/swcEd4Pg">population mean</a> from a <a href="/facts/Sampling_distribution/50dJL1ja">sampling distribution</a> of <a href="/facts/Sample_mean/Ah8VVVDT">sample means</a> if the population <a href="/facts/Standard_deviation/qSug9BRl">standard deviation</a> is unknown. It is also used along with <a href="/facts/P-value/InnYd1bz">p-value</a> when running hypothesis tests where the p-value tells us what the odds are of the results to have happened.

t-statistic open-in-new

t-statistic