Yes I understand Bayes Rule. Nowhere in the article did they make any such fallacy. They are just stating what features happen to be predictive of fraud. Anyon

Yes I understand Bayes Rule. Nowhere in the article did they make any such fallacy. They are just stating what features happen to be predictive of fraud. Anyone actually using this would use a proper statistical model and real data, not random figures they found on a blog. But this post does give useful advice of what features to test. In any case, if there is only a 50% chance that a user is a fraudster, it still might be worth rejecting them. Anyway, using your assumption of 1% fraud, and using all of the features listed in the article (and presumably there would be other features too, but let's just use the ones they talked about), I do the following calculation: 1% = 1:99 x 8:1 (32 bit OS) x 9:1 (fresh cookies) x ? (Null values doesn't give a number, or suggests that 100% of null values are from fraudsters and never appear organically, so I will exclude it) x 5:1 (referrer history) x 96:70 (Windows) x ? (Number for plugins not given, but it seems to be a low correlation) x ? (No number given for incognito, but it's probably low.) = 5:1 or 83% probability they are fraudulent, excluding the missing values, which would raise it further.