[_Nat_]

The title's misinformation: effect-size ISN'T more important than statistical significance.

The article itself makes some better points, e.g.

> I worry that because of statistical ambiguity, there’s not much that can be deduced at all.

, which would seem like a reasonable interpretation of the study that the article discusses.

However, the title alone seems to assert a general claim about statistical interpretation that'd seem potentially harmful to the community. Specifically, it'd seem pretty bad for someone to see the title and internalize a notion of effect-size being more important than statistical significance.

[spywaregorilla]

Not so fast. If you win your first jackpot on the first ticket. You'll require 500,000 failures (at $1 per ticket) in order to fail to reject the null hypothesis at p < 0.05. Assuming you're just doing a t test (which isn't really appropriate tbh).

If you bought just ten tickets you would have a p value below 0.0000001

And that makes sense, because a p value of 0.01 says the probability of getting a sample this far from the null hypothesis is less than 1 in a million by random chance... which is what happened when you got the extremely unlikely but highly profitable answer.

edit: post was edited making this seem out of context...