I am looking to understand alpha error calculation given power, sample size, and effect in a two-sided independent mean comparison. So the hypothesis is:
H0: mean1 = mean2 H1: mean1 != mean2
Assume we know the power, sample sizes, means and standard deviations for each group. From standard deviations, we can calculate a pooled standard deviation, and given means and pooled deviation, we can calculate effect size. Then, from there we can calculate non-centrality parameter.
I am good until this point. But from then on, for a two-tailed t-test, I am not sure how to go about reaching critical t value and find alpha.
I am looking into either an explanation or any type of resource that helps me calculate this by hand. I am aware that I can do this via G*Power:
- Selecting t-test family
- Means:Difference between two independent means (two groups)
- Type of power analysis: Criterion - Compute required alpha-given power, effect size, and sample size.
However, there is no explanation step by step how this is calculated for a two-sided test and I am very confused. I would like to understand this better.
Any help is appreciated.