Just in general, but we are a biostats class if that makes anything different. We define Type 1 Errors as "false positive" and alpha as "the highest risk of making a false positive error." I asked for clarification today, and she it's because as you increase the sample size, you're making it...
Okay -- that makes a lot of sense. I think I can conceptualize why we care about the probability of outcomes that do not occur. Nonetheless, I still don't quite understand what my professor is saying. We defined alpha as the researcher's tolerance for false positives, if that helps? This is copy...
Sorry if I was unclear. I didn't mean p in this case to be probability, I meant p as a Student T-test. Like p<alpha. My main question is if type 1 error increases with sample size.
My professor is teaching us that type 1 error increases with sample size if you keep alpha constant, and I think I understand what she's getting at, but I can't find anything online that supports the idea. Here's what I'm thinking:
We accept that there is an equal chance that a flipped coin...