Statistics has gotten a bad rap. People love to quote Mark Twain (“There are lies, damn lies, and statistics,” alternatively attributed to Benjamin Disraeli), Vin Scully (“Statistics are used much like a drunk uses a lamppost: for support, not illumination”), or Stephen Leacock (“In ancient times they had no statistics so they had to fall back on lies”).

For statisticians, these jokes have become quite tedious. Avoiding small talk at cocktail parties where quips are likely to come up or lying about one’s profession (“I’m a kind of mathematician” sometimes works) are not really satisfying alternatives to the lines that people have saved to shower on the innocent professional. What’s a statistician to do?

Unfortunately, since the application of statistics is indeed frequently misunderstood or misused, many people’s perceptions of statisticians are colored by their certainty that statistics represents a specious approach at best, and that statisticians are mere liars.

As a recently-released book by Tyler Vigen points out, statistics are indeed habitually misused, especially when it comes to understanding correlation. The statistician’s mantra, “Correlation does not equal causation,” falls on the deaf ears of those who insist that correlation does indeed equal causation. Charts show this, don’t they? Vigen shows the dramatic correlation between rates of margarine use, for example, and divorces per 1,000 people. These rates have nearly identical paths, with a correlation of 98.9 percent.

**Source:
Vigen, Tyler , Spurious Correlations (New York: Hatchette Books, 2015), p. 7.**

Clearly, the ups and downs of margarine use have nothing to do with divorce rates—that’s common sense, except maybe in some fantasy best seller. Both statistics may be accurate, but drawing a conclusion that they correlate is an irresponsible use of statistics.

To discover correlation of factors, scatter diagrams are a useful way to begin to analyze data, but cannot suggest specific causal relationships that demand further analysis. In a real estate example, two scatter diagrams indicate correlations between a) square feet and selling price of a home, and b) number of bedrooms and selling price:

One might see that the influence of square footage on selling price seems to be clear, since the slope of the line slopes up and to the right. The influence of the number of bedrooms is not quite as clear: there is a correlation, though, demonstrated by an upward slope of a trend line. One point that must be made is that these two factors—number of bedrooms and amount of square footage—are a least in the same context, and the attributes being studied are from the same house, so it is clear that they are related. The problem comes when causation is attributed to unrelated factors. Data for the number of films Nicholas Cage has made and the number of swimming pool deaths may reflect similar or even exact patterns, but there can be no assumption of correlation or causality, unless one is into wild conspiracy theories.

Statistical analysis yields critical information that supports data-driven decision making. But this analysis, like a surgeon’s knife, must be applied carefully and with the skill to understand its proper application to available data. It’s as simple as that. Statistics has gotten a bad rap because of malpractice—and not by statisticians.

Today, no meaningful research can be undertaken without understanding DOE and the science behind it. Even a new drug molecule or Higgs’ boson have to be confirmed by statistical evidence. While Boltzmann committed suicide because he was ridiculed for bringing statistics into Physics/Chemistry, today Quantum Physics is all about probabilities. Even the father of Bosons, Satyendranath Bose was a statician.