Should I use Bonferroni correction when making two linear contrasts with Fit Model?

[#1]

I'm conducting a series of univariate REML models and have two preplanned orthogonal contrasts. I am wondering if I need to correct the acceptable alpha to guard against Type I error, or if this correction is taken into consideration by the JMP program when I add a second contrast?

[#2 · Accepted solution]

JMP doesn't automatically make the adjustment when you use Custom Test from the Estimates menu to add additional contrasts. You may apply the Bonferroni correction. (As a note, when there are a large number of comparisons, the Bonferroni correction can be too conservative.)

[#3 · Accepted solution]

A slightly less conservative option is the Bonferroni-Holm procedure:  the smaller p value needs to be tested on the alpha/2 level first, the other p value can then be tested on your planned  alpha level if the first test was statistically significant.

A further option for dealing with the multiple testing issue is the false discovery rate approach (FDR-approach) for which a nice and easy usable JMP Add-In is available.

[#4]

Thanks for mentioning the FDR Pvalue add-in. It has been incorporated into JMP known as the Response Screening under Modeling.

[#5]

Thanks so much for the suggestions, that all definitely helps!