How Valid Are Social Vulnerability Models?

The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests. The routine use of this test has been criticised as deleterious to sound statistical judgment, testing the wrong hypothesis, and reducing the chance of a type I error but at the expense of a type II error; yet it remains popular in ophthalmic research. The purpose of this article was to survey the use of the Bonferroni correction in research articles published in three optometric journals, viz. Ophthalmic & Physiological Optics, Optometry & Vision Science, and Clinical & Experimental Optometry, and to provide advice to authors contemplating multiple testing.

Multiplicity of data, hypotheses, and analyses is a common problem in biomedical and epidemiological research. Multiple testing theory provides a framework for defining and controlling appropriate error rates in order to protect against wrong conclusions. However, the corresponding multiple test procedures are underutilized in biomedical and epidemiological research. In this article, the existing multiple test procedures are summarized for the most important multiplicity situations. It is emphasized that adjustments for multiple testing are required in confirmatory studies whenever results from multiple tests have to be combined in one final conclusion and decision. In case of multiple significance tests a note on the error rate that will be controlled for is desirable.