I was hoping someone could help me out with this. I've seen similar questions on the forum, but I need to know if I've understood the correct rationale and procedure for bootstrapping for my particular need.
In my study, I have two independent groups of equal sample size (22 individuals in each group). I am interested in the difference between these two groups, so I've run a simple t-test. The resulting t-value was 1.39, with a p value of .17. The assumptions of the test look good; Equal variance between groups, and variable is close to normally-distributed (but sample sizes are small).
My supervisor advised me to run "simulations" on the test statistic, since the p value was relatively low (to avoid a type-II error). After reading up on resampling techniques, I've concluded that it is feasible to bootstrap the test-statistic, and then explore the range of t values and associated p values to determine the precision of my estimate. Could someone please verify if this is an appropriate rationale for bootstrapping?
As I understand, one procedure for this would be to: a) randomly extract (With replacement) 22 cases for each of my two Groups, b) perform a t-test on these new samples, c) repeat e.g. 1000 times, d) look/plot the ranges of t and p values, and use these to infer if there is a difference between my groups. I am planning on using R for bootstrapping.
Would this be an appropriate way of bootstrapping in my example? I've read that one can bootstrap data directly, or bootstrap just the test statistic. I guess I really want the latter one, but not sure what the difference between these two approaches are.