In hypothesis testing, the alpha value is the probability of making a Type I error. This is the type of error mentioned in the question.
Type I error - you find a difference when a difference doens't really exist.
One way of remembering this is that this is what scientists "want" to make: they want to find a significant difference in their data, thus it is the "first mistake" they'd make.
Alpha is the probability you are willing to accept that you could have made a type I error (i.e. an alpha value of 0.05 means there is a 5% probability you could make a type I error and reject the null hypothesis when you should not)
Type II error - you do not find a difference when you should have because a true difference really exists
Beta is the probability that you make a type II error
Power is equal to (1 - beta)
Power can be increased by increasing sample size, and thus with a larger sample you have a lower probability of making a Type II error
Power can also be increased by increasing expected effect size or increasing precision. It is interesting to note accuracy has no effect on power.
submitted by โcassdawg(1780)
In hypothesis testing, the alpha value is the probability of making a Type I error. This is the type of error mentioned in the question.
FA2020 p263