Statistics experts, what is the best trick or little "story" for remembering the definitions of Type 1 errors and Type 2 errors?

Answer 1

The null hypothesis assumes that nothing has changed or there is no difference between groups. I like to think of this as type 1 conditions, since it is our default. If type 1 means there truly is no change/difference, then a type 1 error means there was no change/difference but the researcher declared there was. A type 1 error is rejecting a null hypothesis that is true.

I think of type 2 being the alternative, where there truly is a change/difference. Therefore, a type 2 error is declaring that there is no change/difference when there really is one. A type 2 error is failing to reject a null hypothesis that is false.

Of course, it never hurts to draw a grid to lay out the options when in doubt. However, I find that when thinking of it in this way, my clients and I have a chance at reasoning through it with less mental gymnastics.