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Comment Re:Bayesian statistics (Score 1) 375

Instead of using the 99.99% figure, use natural frequencies to describe it. This blog post on the NYTimes talks about how people have a much easier time understanding frequencies than prior probabilities. This isn't just a problem of education, cognitive scientists already know that some representations are much easier to reason with than others, even if they are equivalent. For example, many people get this problem wrong:

The probability that one of these women has breast cancer is 0.8 percent. If a woman has breast cancer, the probability is 90 percent that she will have a positive mammogram. If a woman does not have breast cancer, the probability is 7 percent that she will still have a positive mammogram. Imagine a woman who has a positive mammogram. What is the probability that she actually has breast cancer?

But, a lot of people get this framing right:

Eight out of every 1,000 women have breast cancer. Of these 8 women with breast cancer, 7 will have a positive mammogram. Of the remaining 992 women who don’t have breast cancer, some 70 will still have a positive mammogram. Imagine a sample of women who have positive mammograms in screening. How many of these women actually have breast cancer?

Comment Nothing New Here (Score 1) 59

This result was already pretty well known.

Jagatic and others saw this in 2007 in their work on social phishing at Indiana University.

We saw the same in our PhishGuru work at Carnegie Mellon, on training people not to fall for phishing scams in 2009.

As an aside, I know many slashdotters don't believe you can train people to protect themselves from phishing. That is the standard conventional wisdom in computer security. However, we've actually demonstrated that you can, if you make it fun, timely, and relevant. We're commercializing some micro games for security training and a service for simulated phishing attacks based on research we did at Carnegie Mellon.

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