As a researcher, you’re no stranger to the world of statistical significance. You set your alpha level to 0.05, run your tests, and hope that your results will magically appear below that threshold. But what happens when your type 1 error rate starts creeping up above 0.05? Is it still acceptable, or do you need to go back to the drawing board?
I recently came across a Reddit post from a researcher who was struggling with this very issue. They had developed two statistical methods to detect an effect in a physiological study, but when they simulated 1000 data sets, one of their methods had a type 1 error rate of 0.073, while the other had a rate of 0.063. Ouch.
So, is this still within limits? The short answer is no. A type 1 error rate of 0.05 means that there’s a 5% chance of rejecting the null hypothesis when it’s actually true. If your rate is higher than that, you’re essentially increasing the chances of false positives.
But here’s the thing: a type 1 error rate of 0.063 isn’t the end of the world. It’s not ideal, but it’s not a deal-breaker either. The real question is, what’s causing this higher error rate? Is it a problem with your method, or is it something else entirely?
In this case, the researcher discovered that the problem wasn’t with their method, but with their fake data. They had used a fixed seed for one of the random values, which introduced a bias into the overall dataset. Oops.
So, what can you take away from this? First, make sure you’re generating your fake data correctly. And second, if your type 1 error rate is higher than you’d like, don’t panic. Take a closer look at your method and your data to see if there’s something going on that you can fix.
## Final Thought
Type 1 error rates are important, but they’re not the only thing that matters. If you’re getting a higher error rate than you’d like, don’t be afraid to dig deeper and see what’s going on. And remember, a little bit of detective work can go a long way in ensuring the validity of your research.