Why carve up your continuous data?

Continuous data are commonplace in social, biophysical, and health research. For various reasons, researchers often carve up data into ordered chunks. Such data carving results in less information being carried by the data, a reduction or spurious increase in statistical power, and resultant Type I or Type II errors. We give examples of data carving in selected nursing literature, and illustrate how unnecessary categorization can produce erroneous statistical results. Finally, we propose credible alternatives to data carving.