How we position things greatly influences the outcome. In the April 7, 2012 edition of The Economist the article, “Dressing Up,” uncovers that women’s sizes have inflated by four sizes since the 1970’s. The fashion industry calls this “vanity sizing.”
Unlike men’s sizing which is based on inches, women’s sizing is purely arbitrary and often varies by brand. Thus, depending on the size, a pair of women’s pants might have increased as much as four inches at the waist and three inches at the hips since then.
The generally accepted assumption for allowing this size inflation is that if consumers feel good about themselves they are likely to buy. However, even though it seems like a topic to take lightly or with which to have fun, vanity sizing plays in all aspects of statistics. That is why it’s important to challenge definitions and assumptions in order to understand and solve problems.
For instance, the article “Botox and Beancounting” of the The Economist’s April 27, 2011 edition, discusses how official U.S. economic statistics might be overinflating its performance relative to Western European economies. Ironically, the article’s title makes an appropriate analogy to vanity sizing.
U.S. unemployment figures present another excellent example. They not only conflict with one another on occasions but they are difficult to figure. Additionally, their accounting changed in the 1980’s, making them appear lower than before.
Thus, while it’s commonly said that “numbers don’t lie,” that’s true; however, an ignoramus isn’t lying either if he believes his own ignorance. If we’re ignorant to numbers’ origination, we are more likely to accept them if they tell us our glass is half full rather than half empty, thus reinforcing our own perceptions . . . also known as “vanity believing.”