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Avoiding the eyeballing fallacy: Visualizing statistical differences between estimates using the pheatplot command
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AI
DOI：10.1177/1536867X251322962.png)
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摘要
En 中文
Graphical representations of coefficients and their confidence intervals are increasingly used in research presentations and publications because they are easier and quicker to read than 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 errors. To help avoid this eyeballing fallacy, we introduce the pheatplot postestimation command, which visualizes 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 continuous measures rather than binary thresholds, it provides the flexibility to move beyond arbitrary cutoffs of statistical significance. This article offers some examples that illustrate the functionality of the pheatplot command.
Keyword:
gr0099
pheatplot
heatplot
lincom
coefplot
eyeballing fallacy
confidence intervals
p-values
statistical significance
