Let me illustrate this with a deliberately oversimplified model. Let's begin by assuming a total population of 100,000, that's divided into two groups, a 10% high-risk group and a 90% low-risk group. Let's say that the high-risk group has a 60% risk of being attacked, and as a result 40% of its members carry guns. And let's say that the low-risk group has a 5% risk of being attacked, and as a result 3% of its members carry guns. Let's also imagine a total population of 100,000 (just to make the numbers easier), and let's assume that possessing a gun has a modest protective effect for both groups — it reduces the risk of being injured when attacked from 75% to 60%.
Here's what this turns out yielding, with "A" meaning "armed subgroup" and "U" meaning the unarmed subgroup.
Group Number of people in group Probability of being attacked Armed subgroup fraction Armed subgroup number Armed subgroup injury risk Armed subgroup number injured Unarmed subgroup number Unarmed subgroup injury risk Unarmed subgroup number injured High-risk 10000 0.6 0.4 4000 0.36 1440 6000 0.45 2700 Low-risk 90000 0.05 0.03 2700 0.03 81 87300 0.0375 3273.75 Total 100000 0.067 6700 0.227015 1521 93300 0.064027 5973.75 Odds 0.293686 0.068407 The result: The armed subgroup has 3.5 the risk of injury compared to the unarmed subgroup, and the relative odds ratio between them is 4.29. And this is so even though in the model gun possession decreases the injury risk for both the high- and the low-risk group.
Naturally, this is just a model; the real numbers are likely very different from the ones I give here, and in fact no-one knows what the real numbers are.
Indeed, this is the same sort of problem we get with drug testing, and any number of other statistical tests.
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