Plastic Surgery Residents' Understanding and Attitudes Toward Biostatistics: A National Survey.

This study found that plastic surgery residents felt that knowledge of biostatistics was important, however upon objective testing they only had a fair understanding of statistical principles. The residents had difficulty with study study design, ANOVA, regression, and identification of a statistically significant result. Confidence was not a good predictor of objective performance.  (Ann Plast Surg. 2015 Jan 30.)

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Bonferroni multiple comparison test

When performing multiple tests of significance upon a single dataset, there is an ever increasing chance that at least one of these tests will be statistically significant. For example, a p-value of 0.05 means there is a 1-in-20 likelihood of the statistical result occurring by chance alone.  If we perform 20 such tests, then there is a 1-in-1 likelihood of at least one result having a p-value of 0.05 or less.

The Bonferroni correction is simple. When multiple tests of significance are performed, then the cutoff p-value for statistical significance is set to equal 0.05 divided by the number of tests performed.

For example, if 10 tests of significance are performed, the cutoff p-value for statistical significance would be 0.05 / 10 = 0.005 when using the Bonferroni correction. If 5 tests are performed, then the cutoff p-value would be 0.05 / 5 = 0.01.