Lady, Your Slip is ShowingFebruary 19, 2002
by Wendy McElroy, firstname.lastname@example.org
A Jan. 23 congressional study claimed that salaries for women managers in seven out of 10 industries examined had declined from 1995 to 2000. Uncritical newspapers rushed to announce that the wage gap between men and women had widened.
But National Review columnist Betsy Hart took the time to examine the study commissioned by Reps. Carolyn Maloney, D-N.Y., and John Dingell, D-Mich. She found it to be a "biased and highly-emotionalized reinterpretation" that the "creative" staff of Maloney and Dingell had imposed upon otherwise straightforward data. The reinterpretation allowed Maloney to label the study explosively as "a wake-up call" for America and to hint at the need for more federal regulation in the workplace.
Hart phoned Maloney's office, identified herself, and spoke directly to the congresswoman who mistakenly assumed the journalist was also a liberal feminist. Maloney explained that the existence of wage discrimination was considered a fact and the study had been a search for the supporting data. Then, in Hart's words, "Maloney ... shared with me her intention to keep the Right from finding out what she and Dingell are up to." After all, she didn't "want to scare the right wing so that they stop collecting data" on women and the workplace.
Anyone familiar with what passes for statistics within feminism will not be surprised by the willful corruption of data. An underlying assumption of data-manipulators is that people are too stupid to notice the sleight-of-hand. The media exacerbates the problem by not asking the most basic questions about statistics, even ones with surprising conclusions. For example, journalists rarely ask, "What is the margin of error?" or "Does the average reflect a mean or a median?" Public schools contribute their share by failing to teach the fundamentals of statistical analysis.
It is important to guard against those who twist data and, then, wield the results as political weapons. Every statistic should be required to answer several questions before you accept it:
1. Who says so? This inquires into the possible bias of the researchers. For example, Maloney's staffers might well be biased toward processing data in a manner that supports legislation the congresswoman favors. The source of their income doesn't invalidate what they say, but it does call for taking a closer look at their data.
2. How do they know? Unbiased researchers may employ a sloppy methodology that comes from laziness or error. For example, a much-cited study entitled Prostitution, Violence and Post-Traumatic Stress Disorder, collected data from streetwalkers in four "strolls" that were notorious for drug use and violence. Yet the conclusions of the study comment on all prostitutes, including high-paid call girls. In short, it uses an unrepresentative sample to draw broad conclusions about a general population.
3. What's missing? Always place the data within a proper context. The GAO data upon which the congressional "study" is based openly states its limitations: It does not control for highly significant wage factors such as "years of continuous presence in the workforce." As Hart comments, "studies which do control for all relevant factors continually show that the wage gap between men and women virtually or totally disappears."
4. Does the conclusion make sense? Do not let statistics displace your common sense. Consider a "fact" popularized several years ago by feminist Naomi Wolf: 150,000 American women die each year of anorexia. According to the Centers for Disease Control and Prevention, this would make anorexia the fourth-leading cause of death in both males and females. Yet the CDC missed that data. The grossly inflated number had been taken from a newsletter of the American Anorexia and Bulimia Association, which claimed 150,000 to 200,000 women "suffered" from anorexia nervosa. The actual death rate is closer to 100.
5. Did someone change the subject? Researchers often redefine terms in such a manner as to produce desired results. For example, by the word "rape" most people mean forced intercourse. But feminist studies frequently include all sexual assault under that label. In turn, sexual assault is sometimes expanded to include harassment. Popular statistics — e.g., "one in four female college students will be victimized by rape or attempted rape" — must include the definition of "rape" being used in order to be meaningful.
Finally, and most importantly, remember that a correlation does not indicate cause and effect. A correlation is a mutual relationship between A and B — for example, if one goes up, then the other goes down. A cause-and-effect relationship means that A causes B. Consider the claim that women make 75 percent as much as men for doing the same job. The statement draws a correlation between being a woman and earning power but it says nothing about cause and effect. The 75 percent (if true) may be caused by other factors not weighed by the study. For example, women often leave the workplace to have children. This factor alone may cause much of the wage gap.
In his definitive yet delightfully simple book How to Lie With Statistics, Darrell Huff observes, "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify."
Yet statistics are too useful to dismiss. Instead, the secrecy should be removed.
With Huff tucked under your arm, unafraid "right-wingers" should approach Maloney's statistics, ask how they are funded, whether the conclusions are overbroad, what is their context, how does she define all relevant terms, and is it a correlation rather than a cause-and-effect? Develop this level of skepticism toward data, and four out of three times you won't go wrong.