A Type 1 error is rejecting H0, when it’s true in reality A Type 2 error is not rejecting H0, when it’s false
beta - probability of not rejecting false hypothesis alpha - probability of rejecting true hypothesis
The higher alpha the lower is beta Higher alpha means we are rejecting hypothesis more often which means we do less Type 2 errors. But it also means we make more Type 1 errors Lower alpha means we are rejecting not so often so we make less Type 1 errors and more Type 2 errors. In statistics we prefer make less Type 1 errors at the cost of making more Type 2 errors thus we choose really smal alpha values (0.05) In other words we prefer to not reject false hypothesis more then rejecting true hypthesis