Hi all, I'm reviewing a paper that employs a strange (to me) approach to a non-parametric significance testing. I'm familiar with permutation tests and bootstrapping confidence intervals around parameter estimates, but here they seem to be bootstrapping CIs for a manufactured Null F-value. They have a simple 3 groups between-Ss design and compute the F-value of the observed data. Then, they re-center each group's mean to zero then repeatedly: randomly resample values from each group (with replacement, doubling each group's size) and re-compute the F-value. The distribution of re-centered/resampled F-values is then used as a reference distribution within which the percentile of the observed F-value is computed. They determine that the observed F-value is outside the 95% confidence interval of the re-centered/resampled F-values and conclude a significant effect of group.
In my head this makes some sense (though I'd think a straight permutation test would be a lot simpler), but having never heard of anything like this before I thought I'd see what others on this list think of the approach. Mike -- Mike Lawrence Graduate Student Department of Psychology Dalhousie University Looking to arrange a meeting? Check my public calendar: http://tr.im/mikes_public_calendar ~ Certainty is folly... I think. ~ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.