An interesting pint Cory Doctorow brought up in his novel, Little Brother, is the idea of the "false positive." He writes, "Say you have a new disease, called SuperAIDS. Only one in a million people gets SuperAIDS. You develop a test for SuperAIDS that's 99 percent accurate... You give the test to a million people. One in a million people have SuperAIDS. One in a hundred people that you test will generate a 'false positive' the test will say he has SuperAIDS even though he doesn't. That's what '99 percent accurate' means: one percent wrong... If you test a million random people, you'll probably only find one case of real SuperAIDS. But your test won't identify one person as having SuperAIDS. It will identify 10,000 people as having it" (128).
This idea can be linked to Michael Morris' essay on student data mining. Critics of Morris' argue that looking at students' data would not be an effective method of school shooting prevention, as many innocent behaviors can be seen as "suspicious." Even if looking into student data is deemed 99% effective in detecting threatening individuals (Which it is not. In fact, it is most likely nowhere near that statistic), the false positive theory explains that many more non-suspicious students will be marked as suspicious than actually suspicious people. However, one can argue that the pros of these "threat tracking" methods outweigh the cons. If data surveillance can prevent a dangerous school attack, then it is worth identifying a couple innocent people as suspicious. (This opinion can be seen as a bit Machiavellian.)
The paradox of the false positive can be applied to beyond data encryption. One can use this idea to examine how misleading statistics are in general. For example, hand sanitizer claims to kill 99.9% of bacteria. There's about 1500 bacterial cells living on each square centimeter of your hands. If 99.9% of those bacterial cells are killed off by hand sanitizer, there's still several billions left, and the ones left are probably the strong ones capable of making you sick.