By trade, historians tend to be skeptics who prefer the specificity of nitty-gritty facts to grand generalizations and fanciful speculations. Such skepticism seems especially appropriate when dealing with claims based on quantitative analysis. Although people often think of numeric data as "hard" evidence, there is also a common perception that experts can make numbers "say" anything they wish. As the aphorism attributed to Mark Twain (among others) declares, "There are three kinds of lies: lies, damned lies, and statistics." One may be tempted to dismiss quantitative analysis because it seems obscure and hence untrustworthy. Yet the information available in numeric form can be too valuable for a good historian to ignore. Quantitative data do not speak for themselves, but with a little coaxing they can sometimes tell us things about the past that we cannot discover in "qualitative" kinds of evidence.

The challenge for beginning historians is twofold: (1) to learn how to pose good questions of available quantitative sources, including both raw and aggregated data; and (2) to learn how to organize and "read" the data yourself to answer the questions you have posed. If you do not like mathematics, you probably will not become a heavy-duty quantitative historian. But you can still use basic quantitative methods in your research, and you can still become a critical reader of complicated quantitative scholarship. There is a range of reasonable positions between that of a true believer, on the one hand, and an anti-numeric nihilist, on the other. The philosophy underlying this guide is that quantitative history is too important to be left exclusively to the mathematically inclined.