Statistics and Lies

I was recently having a discussion with a group of students, specifically about Marvin Harris’ discussion of the importance of statements of co-variance and his call for a more statistically oriented anthropology in The Rise of Anthropological Theory (affectionately – or disaffectionately – referred to as The RAT during my time as a master’s student at the University of Georgia).

One student objected that “Statistics are basically just lies.”

I was a bit taken aback by this.

Statistics can be used to mislead or distort things. For example, it’s fairly common to encounter figures on median income for U.S. households in the mainstream mass media. There’s no particular reason to doubt the accuracy of such figures in most cases, but one could begin to wonder why reportage of mean household income is much less common, much less why the two central tendency measures are so rarely seen together. But statistics per se aren’t lies.

Statistics involves a set of analytical tools and ways of thinking about sets of data. As with any other tool, statistics can be misused. But saying that statistics are lies because they can be used to lie strikes me a bit like saying that words are inherently lies because words are used to lie. (There are some who think that – but they’re lying.)

Still, there is a real and strong distrust of statistics among many cultural anthropologists and scholars in the humanities disciplines. This seems to me to derive from the now old (and tired) divide between “quantitative” and “qualitative” scholarship and the strong mutual distrust that has permeated that divide.

I’ve written before that this is a false divide. There is no non-quantitative research. All scholarship involves an awareness of quantity, whether in the binary mathematics of presence/absence; rough quantification along the lines of something being present in small or large amount, or happening frequently, continuously, or infrequently; or the highly enumerated quantification of precise counting. There is no non-qualitative research. All scholarship involves choice of what to pay attention to, count, etc.

Moreover, the emphasis on the qualitative/quantitative labels tends to obscure what all good scholarship shares in common, which is measurement and interpretation (see “Measurement and Interpretation”). If one moves past the qual/quant divide (the sort of attitude of “I’m not the sort of scholar who does statistics” or “I’m not the sort who pays attention to anything that can’t be quantified” [by which most mean enumeration, because again, there’s nothing that’s without quantity]) then a whole range of analytical tools and ways of thinking are opened up as possibilities, to be deployed as best fits the research question at hand rather than as best fits an ideological commitment to being “qualitative” or “quantitative.”