How That 'Extra .9%' Could Ward Off a Zombie Apocalypse
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netbuzz writes "The questioner on Quora asks: 'When is the difference between 99% accuracy and 99.9% accuracy very important?' And the most popular answer provided cites an example familiar to all of you: service level agreements. However, the most entertaining reply comes from a computer science and mathematics student at the University of Texas, Alex Suchman. Here's his answer: 'When it can stop a Zombie Apocalypse.'"
Re:That's not the question (Score:5, Informative)
It's called toxoplasmosis gondii and it makes men violent and women horny.
Rats also get it, and it makes them attracted to the smell of cat piss.
Re:That's not the question (Score:4, Informative)
It's worth noting that the the effects they cause on humans are small enough that they are only detectable when measured across large samples of people, and even then, only to a very small degree. The last time I saw a study on it, the impact was something like a 1-2% difference in reported moods/tendencies across a sample of, I believe, around 100 people, and while I think the report said it was statistically significant, even they admitted that for any particular individual it's nearly impossible that you'd notice any differences between their mood before and after an infection.
Of course, the headline for the article where I first heard about the report was rather sensationalist in nature, and that's what everyone else picked up and ran with, rather than reading the actual findings from the report.
Re:Statistics 101 (Score:5, Informative)
The key factor is that the trait being tested is rare; only one in 500 people has it. In this case, the false positives can still be (substantially) more frequent than true positives.
Say you test 50,000 people. 100 have it, 49,900 don't. Of the 100 who have it, there will be 99 correct 'yes' results and one incorrect 'no' result. And of the 49900 who don't have it, there will be 49401 correct 'no' results and 499 incorrect 'yes' results.
So total, we have 598 'yes' results. But 499 of those are false positives, which is 83.4444%; only 16.5555% of the folks who test positive are really positive.