“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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I almost didn’t make a best-of list this year, but as I clicked through the year’s post, it was hard …
For various occupations, the difference between the person who makes the most and the one who makes the least can be significant.
Before you dive into the advanced stuff – like just about everything in your life – you have to learn the fundamentals before you know when you can break the rules.
The ever so popular Walmart growth map gets an update, and yes, it still looks like a wildfire. Sam’s Club follows soon after, although not nearly as vigorously.