Mary Childs

I had smoking prevalence at the county level.

Abel says that all the established research at the time indicated that smoking bans were hugely effective, that they'd gotten lots of people to stop smoking.

But when Abel crunched his numbers?

I was finding absolutely no effect.

None.

It was like, nobody stops smoking.

I've played with the data for six months, and I find nothing.

And Abel was trying to make a name for himself in academia, which means getting his research published in an academic journal.

And it's harder to get published if you find no effect, especially given that the existing literature did show an effect.

So what Abel needed was something statistically significant.

There's this 95%, 5% cutoff that really matters.

We're obsessed with these thresholds.

So Abel kept tinkering with his dataset, changing his computer code to contort the data one way and then another, until eventually, one day, he found a way to analyze one subset of his data that gave him what he'd been looking for, a result demonstrating that smoking bans had decreased smoking, and a result that was significant.

It was like, there you go.

I was so happy.

I was in the library.

I was like, significant.

I was so happy.

But Abel's happiness did not last long.

Because the more he thought about how he'd gotten that significant result, the more it started to seem like it was working against the whole goal of social science.