Have a look at this. I put together a small site for finding good ingredient combinations based on online recipes. It covers about 3000 normal ingredients and is generated from around 150,000 online recipes with review scores.  There’s single ingredient profiles, a 3d graph browser for visualizing connections and a search feature for multiple ingredient searches.  It’s also a blistering fast site. I dare you to find an equally responsive site these days! 

I’ve always felt that the idea of repeated significance testing error and false positive rates is a bit of a pedantic academic exercise.  And I’m not the only one, some A/B frameworks let you automatically stop or conclude at the moment of significance, and there’s is blessed little discussion of false positive rates online. For anyone running A/B tests it’s also little incentive to control your false positives. Why make it harder for yourself to show successful changes, just to meet some standard no-one cares about anyways? It’s not that easy. Because it actually matters, and matters a lot if you care about your A/B experiments, and not the least about what you learn from them. Evan Miller has written a[…]