The t statistic is used in the context of a t-test when doing hypothesis testing. Hypothesis tests start with a null hypothesis (Ho) which states there is no statistically significant difference.

A t-test is an inferential statistic used in hypothesis testing to determine if there is a statistically significant difference between the means of two samples.

Statistical significance determines that a relationship between two or more variables is caused by something other than chance. It provides a p-value or probability.

Statistical significance alone didn’t lead to the replication crisis. The institutions of science incentivized the behaviors that allowed it to fester. You’ve read 1 article in the last month.

But business relevance (i.e., practical significance) isn’t always the same thing as confidence that a result isn’t due purely to chance (i.e., statistical significance).

Where did the idea for statistical significance come from? Many scientists now interpret P equal to 0.05 as a cutoff between an experiment that “worked” and one that didn’t.

One advocate called it a “surgical strike against thoughtless testing of statistical significance” and “an opportunity to register your voice in favour of better scientific practices”.