Alexi Horowitz-Ghazi

Abel was getting his master's in economics, and he was writing a paper on whether smoking bans in restaurants and workplaces actually made people smoke less.

He collected this huge data set.

For the statistically uninitiated, significant means the result would be produced by chance less than 5% of the time.

So the probability that the result is just random is 5% or less.

That is the cutoff for whether your findings count or not.

Finding a significant result meant that if his paper was published, he would get to put a little asterisk or star next to his results.

And the more statistically significant the result, the more stars you got to claim.

For example, policymakers need to know whether smoking bans work to make sound policy decisions.

But here he was torturing the data to match the preconceived hypothesis.

But because of all that, people were doing what he had done, trimming and squeezing and coaxing the data towards significant results.

And that can easily cross over into a kind of data manipulation called

P-hacking.

P as in probability.

And Abel says it can happen almost subconsciously.

But when Abel and his colleagues started submitting the research for publication, they got a resounding series of no's.

Academic publishing seemed hesitant to open up an empirical reckoning.

After a few years, they did manage to publish their paper in 2016.

They called it Star Wars The Empirics Strike Back.

Do you get it?

Oh, you definitely get it.