Baseline criminality has always been a poor explanation for police shootings, as the density of shootings does not strongly correlate with the density of crime. That is to say, more crime does not necessarily mean more shootings. As such, saying "But x group commits more crime" fails at providing a strong causal explanation, as it fails to establish even a strong correlation.
The most important thing when it comes to data is the question of "What are you going to do with it?" because often, the data doesn't make the argument for itself. As such, you need to be very careful about how you use the data. If you are looking at criminality data and saying "See, this works as an explanation for the disproportionate number of African Americans killed by police," you are using the data to say something that it does not inherently say. As such, you must then ask if the data alone is sufficient to support the hypothesis. In this case, other data indicates that this is not a valid alternate explanation.
One of the most important things that bad actors will do when making an argument, is use a kernel of truth to tell a lie. The most important thing is not proving the voracity of that kernel of truth, it is asking whether it is capable of making the argument that is being made.
Further, even if racial bias was a poor fit to explain the police shooting data, that would not in any way imply that police reform is not important or necessary, or that the current levels of police violence are warranted.
Last edited by sundin13 - on 18 February 2022
