Making Data Apply to Real People
Many of us have uneasy feelings when reading statistics that presumably apply to ourselves and our own lives. Often the statistics do not seem to “fit” and seem to misrepresent the lives of real people from which the statistics are derived. It is with good reason that we chuckle when someone tells us that the average U.S. household has 0.64 children in it.
We were reminded of this upon hearing prominent news reports a few days ago that the average household income in the U.S. has fallen by about 10% in the past decade, most of it happening since the start of the recession four years ago. But does that mean most Americans’ incomes are falling? No. Though it is hard not to think so given how the data are being presented and reported.
The problem is that statistical averages such as means and medians do not tell us what is happening to specific people or groups of people. They are abstract properties of the whole, but they do not directly describe the parts making up the whole.
Unemployment has grown from 4% to 9%. As more people lose their income, the average is pulled down even if a large majority remain employed and see no decrease in their income. There are other factors that pull down the average as well. Some people who lose their jobs are taking new jobs at lower wages. Some people have wage or salary increases, but the increases are lower than the rate of inflation.
So overall (at the aggregate level) it is true that income has fallen. But for most parts of the whole (at the level of individuals), it is probably not true. We should have been told not only averages, but also the percentage of households that have seen a decline in their individual incomes over the last decade. Almost certainly it would paint a different picture.
When we at Versta Research interpret and report market research and polling data, we use percentages far more often than averages for exactly this reason. We are focused on the story that the data tell, which is usually about people not about abstract wholes. It requires careful attention not only to the stats and the data, but also to the interpretation and communication of those stats so that your audience has a realistic picture of what they mean.
—Joe Hopper, Ph.D.