When we teach “
R for statistics” to groups of scientists (who tend to be quite well informed in statistics, and just need a bit of help with R) we take the time to re-work some tests of model quality with the appropriate significance tests. We organize the lesson in terms of a larger and more detailed version of the following list:
- To test the quality of a numeric model to numeric outcome: F-test (as in linear regression).
- To test the quality of a numeric model to a categorical outcome: χ2 or “Chi-squared” test (as in logistic regression).
- To test the association of a categorical predictor to a categorical outcome: many tests including Fisher’s exact test and Barnard’s test.
- To test the quality of a categorical predictor to a numeric outcome: t-Test, ANOVA, and Tukey’s “honest significant difference” test.
The above tests are all in terms of checking model results, so we don’t allow re-scaling of the predictor as part of the test (as we would have in a Pearson correlation test, or an area under the curve test). There are, of course, many alternatives such as Wald’s test- but we try to start with a set of tests that are standard, well known, and well reported by
R. An odd exception has always been the χ2 test, which we will write a bit about in this note. Continue reading Adding polished significance summaries to papers using R