4 ms·

I found his version clearer because "%" distinguishes proportions from quantities.

• DanBC 13y ago
  Yes, but imagine you're taking the test. Suppose you have a test that's 90% accurate in identifying both people with X and people without X. If you assume that 4% of people are people with X and you're told that you test positive for someone who has X. Do you really find it easy to arrive at your actual chance (26%) of having X? Let's not forget that most people on HN are at the smarter end of the bell curve. It'd be interesting to see the results of a large scale study about answers to questions like this.
  • 6ren 13y ago
    I think you're misreading my comment. When I said "%" distinguishes proportions from quantities, I was implying there'd be both proportions and quantities (as his version has). Otherwise, they needn't be distinguished. When I said I found his version clearer, I meant between the two versions originally given. The one you've just added is of course less clear because unlike the other two, it doesn't point out the issue. BTW: But maybe it is clearer, for calculation rather than understanding, because I get 27.(27)%, not 26%... https://www.google.com/search?q=%28.9*.04%29/%28.9*.04%2B.1*.96%29= https://www.google.com/search?q=%28.9*.04%29/%28.9*.04%2B.1*...
  • apalmer 13y ago
    not sure how to calculate this with percents true positives .90 * .04 = 0.036 false positives .10 * .96 = 0.096 total positives 0.132 positives that are true positives 0.036 / 0.132 = 0.2727... i had to think about the calculation as i was doing it wasnt automatic even though it was just multiplication, but I think the difficulty is more to do with the fact that you have to use some relative of bayesian probability not really the fact that you had to deal with percentages