 Type 2 error. Not finding something that is there. False negative, right? So in the experimental world, again, this would be your independent variable had an effect, but you didn't find the effect. Ah, it's kind of weird, right? So this is, this is the one you really don't want. You don't want to not be sensitive to the effect of your independent variable, not be sensitive to the effect of a test or something like that. You really, really, really want to avoid type 2 error. Again, false negatives. These can be very difficult. Let me just set up an example for you that I often use in the medical world. If I don't find that you have HIV, even though I've given you a test, if so, the test comes back negative, but you really do have HIV in the real world, but my test tells me you don't. Guess what? You're gonna go out and do all sorts of naughtiness and you're gonna be spreading that disease. That's dangerous. We don't like type 2 error. The same problems happen in the, in the lab. It's just a little less serious, right? But scientifically and research-wise, it does make things challenging. You do not want to end up in a scenario where you didn't find something that really did happen or you didn't find an effect that really was there. It leads you down a bad path of thinking that the independent variable was, independent variable was ineffective. So, there you go. Have a good day.