In answering a question, scientists sometimes collect data that answers a different, sometimes yet more important question. And when that happens, scientists sometimes overlook the easter egg. This recently happened to me, or so I think.

Kabir and I recently investigated the extent to which estimates of motivated factual learning are biased (see here). As part of our investigation, we measured numeracy. We asked American adults to answer five very simple questions (the items were taken from Weller et al. 2002):

- If we roll a fair, six-sided die 1,000 times, on average, how many times would the die come up as an even number? —
*500*
- There is a 1% chance of winning a $10 prize in the Megabucks Lottery. On average, how many people would win the $10 prize if 1,000 people each bought a single ticket? —
*10*
- If the chance of getting a disease is 20 out of 100, this would be the same as having a % chance of getting the disease. —
*20*
- If there is a 10% chance of winning a concert ticket, how many people out of 1,000 would be expected to win the ticket? —
*100*
- In the PCH Sweepstakes, the chances of winning a car are 1 in 1,000. What percent of PCH Sweepstakes tickets win a car? —
*.1%*

The average score was about 57%, and the standard deviation was about 30%. Nearly 80% (!) of the people couldn’t answer that 1 in a 1000 chance is .1% (see below). Nearly 38% couldn’t answer that a fair die would turn up, on average, an even number 500 times every 1000 rolls. 36% couldn’t calculate how many people out of 1,000 would win if each had a 1% chance. And 34% couldn’t answer that 20 out of 100 means 20%.

If people have trouble answering these questions, it is likely that they struggle to grasp some of the numbers behind how the budget is allocated or for that matter, how to craft their own family’s budget. The low scores also amply illustrate that the education system fails Americans.

Given the importance of numeracy in a wide variety of domains, it is vital that we pay greater attention to improving it. The problem is also tractable — with the advent of good self-learning tools, it is possible to intervene at scale. Solving it is also liable to be good business. Given numeracy is liable to improve people’s capacity to count calories, and make better financial decisions, among other things, health insurance companies could lower premiums in lieu of people becoming more numerate, and lending companies could lower interest rates in exchange for increases in numeracy.

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