4 Statistical Reasoning in Everyday Life

Module 4 Introduction

Statistical Reasoning in Everyday Life

Asked about the ideal wealth distribution in America, Democrats and Republicans were surprisingly similar. In the Democrats’ ideal world, the richest 20 percent would possess 30 percent of the wealth. Republicans preferred a similar 35 percent (Norton & Ariely, 2011).

IN DESCRIPTIVE, CORRELATIONAL, AND EXPERIMENTAL research, statistics are tools that help us see and interpret what the unaided eye might miss. Sometimes the unaided eye misses badly. Researchers Michael Norton and Dan Ariely (2011) invited 5522 Americans to estimate the percent of wealth possessed by the richest 20 percent in their country. The average person’s guess—58 percent—“dramatically underestimated” the actual wealth inequality. (The wealthiest 20 percent, they reported, possessed 84 percent of the wealth.)

When setting goals, we love big round numbers. We’re far more likely to want to lose 20 pounds than 19 or 21 pounds. We’re far more likely to retake the SAT if our verbal plus math score is just short of a big round number, such as 1200. By modifying their behavior, batters are nearly four times more likely to finish the season with a .300 average than with a .299 average (Pope & Simonsohn, 2011).

Accurate statistical understanding benefits everyone. To be an educated person today is to be able to apply simple statistical principles to everyday reasoning. One needn’t memorize complicated formulas to think more clearly and critically about data.

Off-the-top-of-the-head estimates often misread reality and then mislead the public. Someone throws out a big, round number. Others echo it, and before long the big, round number becomes public misinformation. A few examples:

Ten percent of people are homosexual. Or is it 2 to 4 percent, as suggested by various national surveys?
We ordinarily use only 10 percent of our brain. Or is it closer to 100 percent?
The human brain has 100 billion nerve cells. Or is it more like 40 billion, as suggested by extrapolation from sample counts?

The point to remember: Doubt big, round, undocumented numbers. That’s actually a lesson we intuitively appreciate, by finding precise numbers more credible (Oppenheimer et al., 2014). When U.S. Secretary of State John Kerry sought to rally American support in 2013 for a military response to Syria’s apparent use of chemical weapons, his argument gained credibility from its precision: “The United States government now knows that at least 1429 Syrians were killed in this attack, including at least 426 children.”

Statistical illiteracy also feeds needless health scares (Gigerenzer et al., 2008, 2009, 2010). In the 1990s, the British press reported a study showing that women taking a particular contraceptive pill had a 100 percent increased risk of blood clots that could produce strokes. This caused thousands of women to stop taking the pill, leading to a wave of unwanted pregnancies and an estimated 13,000 additional abortions (which also are associated with increased blood-clot risk). And what did the study find? A 100 percent increased risk, indeed—but only from 1 in 7000 to 2 in 7000. Such false alarms underscore the need to teach statistical reasoning and to present statistical information more transparently.