Wolchover: Yet you were the only student in your math cohort to pursue it academically. Why is that?

Richards: There was a tendency in Jamaica for people to shy away from mathematics because of the economic considerations. Incomes would tend to be substantially smaller than if you were to become, say, a doctor, lawyer, or engineer.

Wolchover: How did you find your way to statistics?

Richards: When I went to the University of the West Indies, mathematics was everything. If I had tried to study physics or chemistry or biology or law or medicine, I would have been a complete failure. I took a course on probability and statistics in my second year from Rameshwar Gupta, who eventually became my thesis adviser. He was an expert in multivariate analysis [which concerns the relationships between many variables], and so that became my field. I’m a great believer in working with people I like. And I could also see that there were lots of interesting mathematics problems working with him, so it was the best of both worlds.

That’s how I got into statistics, but I didn’t really become a statistician until I was fairly close to getting tenure at the University of North Carolina. I knew all the formulas, but the art and joy of analyzing data did not really hit home until then. Before that, I was basically just a mathematician pretending to be a statistician.

Wolchover: What’s the difference between a mathematician and a statistician?

Richards: It’s so hard to decide where the boundaries lie, but I would say that mathematics is a field that specializes in deductive logic: They lay down a bunch of axioms and then they try to deduce the logical consequences of those axioms. Two points define a line, and so on and so forth, and then off you go. Whereas statistics is more the art of inductive logic: We look at the end result and we try to understand what could have caused such an end result.

We “mathematical statisticians” come up with formulas that help with the inductive aspect of our data analyses. Let me try and give you a simple example: Suppose I have a coin in my hand and I ask, is the coin a fair coin or is it biased toward heads or tails? What we would do is toss the coin 100 times, and if the percentage of heads is too far from one half in either direction then we would be inclined to say that the coin seems to be unfair. But the question is, how far is too far? Suppose we got 48 percent heads. How about 40 percent heads? The question of how far from one-half is too far is answered by using various mathematical formulas and probabilistic calculations.

Wolchover: So, how far is too far? I would guess 40.

Richards: You made a very good guess! I am very impressed! This question has consumed people’s attention for centuries. My cardinal rules are: If a coin is tossed 100 times and the number of heads is zero to 40, or 60 to 100, then you have strong statistical evidence that the coin is biased. Moreover, depending on how much money is at stake, if you get 41 or 59 heads, then you’re well-advised to leave the casino immediately. Even at 42 or 58 heads, you should be nervous about the tosser.