best practices

Too Many Colors Spoil the Graph

March 26th, 2024 by

When you draw a graph- either a bar chart, a scatter plot, or even a pie chart, you have the choice of a broad range of colors that you can use. R, for example, has 657 different colors from aliceblue to yellowgreen. SAS has 13 shades of orange, 33 shades of blue, and 47 shades of green. They even have different shades of black.

You have a wealth of colors, but you can’t use all of them in the same graph. The ideal number of colors is 2.


Best Practices for Organizing your Data Analysis

March 21st, 2022 by

There is a lot of skill needed to perform good data analyses. It is not just about statistical knowledge (though more statistical knowledge is always helpful). Organizing your data analysis, and knowing how to do that, is a key skill.  (more…)

Best Practices for Data Preparation

October 4th, 2021 by

If you’ve been doing data analysis for long, you’ve probably had the ‘AHA’ moment where you realized statistical practice is a craft and not just a science. As with any craft, there are best practices that will save you a stage 1lot of pain and suffering and elevate the quality of your work. And yet, it’s likely that no one may have taught you these. I know I never had a class on this. (more…)

Using Predicted Means to Understand Our Models

January 14th, 2019 by

The expression “can’t see the forest for the trees” often comes to mind when reviewing a statistical analysis. We get so involved in reporting “statistically significant” and p-values that we fail to explore the grand picture of our results.

It’s understandable that this can happen.  We have a hypothesis to test. We go through a multi-step process to create the best model fit possible. Too often the next and last step is to report which predictors are statistically significant and include their effect sizes.