Frane (2015) pointed out the difference between per-family and familywise Type I error control and how different multiple comparison procedures control one method but not necessarily the other. He then went on to demonstrate in the context of a two group multivariate design containing different numbers of dependent variables and correlations between variables how the per-family rate inflates beyond the level of significance. In this article I reintroduce other newer better methods of Type I error control. These newer methods provide more power to detect effects than the per-family and familywise techniques of control yet maintain the overall rate of Type I error at a chosen level of significance. In particular, I discuss the False Discovery Rate due to Benjamini and Hochberg (1995) and k-Familywise Type I error control enumerated by Lehmann and Romano (2005), Romano and Shaikh (2006), and Sarkar (2008). I conclude the article by referring readers to articles by Keselman, et al. (2011, 2012) which presented R computer code for determining critical significance levels for these newer methods of Type I error control.
Keselman, H. J.
"Per Family or Familywise Type I Error Control: "Eether, Eyether, Neether, Nyther, Let's Call the Whole Thing Off!","
Journal of Modern Applied Statistical Methods:
1, Article 6.
Available at: http://digitalcommons.wayne.edu/jmasm/vol14/iss1/6