> You can't take a vague dataset and then "correct" it like this without absolutely huge error bars.
That ship has already sailed; Tesla makes active safety claims based on that dataset. To hold research to your standard here would be to say that Tesla can make its claims, but nobody can challenge those claims.
> Why did they all push the results in one direction?
If you have two correction steps, then a priori you'd expect a 25% chance that they both act in the same direction. I don't think this is very remarkable.
> Why not gender?
This probably would be a reasonable addition. I doubt it would change the results much, but we have the well-known fact that insurance rates differ for men and women, so it may be relevant.
> Why not income?
First, is this data even generally available? If the data doesn't exist, then we can't control for it.
Second, should we expect crash rates per mile driven to differ greatly by owner/driver income? A priori, I wouldn't think this demographic quality to have a strong impact.
> Why not compare vs. like cars?
I think "personal vehicle" is a reasonable comparative category. Would we expect collision rates to differ greatly between more specific categorizations? For the sake of the overall conclusion, would we expect Tesla-equivalents to be particularly crash prone in the broad dataset?
Any statistical analysis will have its limitations, but when we're talking about life-safety claims from a manufacturer I think we should have wide latitude to look at critical evaluations of the original data.