Type I and II errors simplified

Posted to Statistics  |  Tags: ,  |  Nathan Yau

“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]

Favorites

Best Data Visualization Projects of 2016

Here are my favorites for the year.

Watching the growth of Walmart – now with 100% more Sam’s Club

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.

Most popular porn searches, by state

We’ve seen that we can learn from what people search …

Where Bars Outnumber Grocery Stores

A closer look at the age old question of where there are more bars than grocery stores, and vice versa.