
Everybody’s Lying about the Link Between Gun Ownership and Homicide - wskinner
https://medium.com/@bjcampbell/everybodys-lying-about-the-link-between-gun-ownership-and-homicide-1108ed400be5
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vannevar
The author's criticism of other analysis have some merit, though his chief
complaint is that the others include suicide and accidental death. But isn't
his point that you shouldn't selectively exclude contrary data? Since those
who die by accident or suicide are just as dead, it's hard to argue for their
exclusion except as another way of "lying" about the relationship between gun
ownership and death by firearm.

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comex
This article accuses others of selectively choosing what to graph, but does
much the same itself. It graphs gun ownership vs. firearm homicide rate for:

1\. All countries

2\. Countries with low firearm homicide rates

3\. European countries

but neglects to graph _rich countries_ , which would make the United States
stick out like a sore thumb in the top-right corner.

It goes on to cite a graph from Vox of precisely that, and criticize it on
various grounds, including its lack of R^2 value - but without providing a
substitute graph, or a substitute R^2 value.

Admittedly, the R^2 would probably still be low (though I don't know _how_
low). And even if the graph has the United States in the top right, a single
data point that's an outlier in two measures would not _itself_ establish a
link between those measures.

But neither does a lack of correlation between the measures in other data
points, by itself, rule out that they're related in the case of the outlier
point. For example, consider the amount of time I spend using my phone in a
particular hour, versus the amount of time I've already spent using it since
its last charge. Most of the time, my guess is those factors are positively
correlated (revealing how non-busy I am that day) or uncorrelated. But on rare
occasions, the latter value approaches my phone's battery life, at which point
the former drops to zero. In other words, it's an outlier case where the
correlation suddenly becomes negative.

To determine whether a connection might exist, it's useful to ask:

\- Whether there is a plausible mechanism for causation (there's a pretty
obvious one in this case), or more generally for the two measures to reflect
aspects of the same underlying processes (i.e. gun culture).

\- Whether there's independent reason to think those processes would act
unusually for that data point (there is, because the U.S.'s gun culture is
unique).

