“Type I” and “Type II” errors, names first given by Jerzy Neyman and Egon Pearson to describe rejecting a null hypothesis when it’s true and accepting one when it’s not, are too vague for stat newcomers (and in general). This is better. [via]
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It’s always tough to pick my favorite visualization projects. Nevertheless, I gave it a go.
Many lists of maps promise to change the way you see the world, but this one actually does.
We’ve seen that we can learn from what people search for, through the eyes of Google suggestions: state stereotypes, national …
A closer look at the age old question of where there are more bars than grocery stores, and vice versa.