Avoiding the eyeballing fallacy: Visualizing statistical differences between estimates using the pheatplot command

Graphical representations of coefficients and confidence intervals of regression coefficients are increasingly used in research presentations and publications due to their easier and quicker readability compared to tables. However, in coefficient plots that include several estimated coefficients, researchers often use confidence intervals to eyeball whether coefficients are statistically significant from each other, which results in an overly conservative test and increased risk of type II error. To help avoid this eyeballing fallacy, we introduce the pheatplot postestimation command, designed to visualize the statistical significance across estimates of categorical variables in a regression model. pheatplot efficiently compares the significance level between point estimates and helps researchers avoid making wrong assumptions about whether estimates differ. Moreover, by representing p-values as a continuous measure rather than a binary threshold, it provides the flexibility to move beyond arbitrary cutoffs of statistical significance. This article offers a series of examples that illustrate the functionality of the pheatplot command.