Earlier quoted context omitted.
From the article: > But zooming out from the specifics of Herzberg's crash, the more fundamental point is this: conventional car crashes killed 37,461 in the United States in 2016, which works out to 1.18 deaths per 100 million miles driven. Uber announced that it had driven 2 million miles by December 2017 and is probably up to around 3 million miles today. If you do the math, that means that Uber's cars have killed…
You have a sample size of 1. Acknowledging that doesn't suddenly make the sample evidence of anything, good or bad :P. Almost all the miles driven are going to be in near-ideal circumstances (daylight, no rain, good road surface, driver familiar with normal road traffic conditions and drives the route regularly). I have nearly no insight into the uber death, but I gather it happened at night. It could easily be that…
Suppose, as a massive oversimplification, Uber's self-driving cars crash with some constant probability P for every mile driven (i.e. a Bernoulli process).
We now have learned at least one thing with absolute confidence: P > 0.
The first mile driven before the accident, of course, also showed P But beyond absolute certainty, we also have a better idea of the actual value of P. (Intuitively, the longer we go without a crash, the lower we suspect P to be, and for every crash, we increase our estimate of P).
If there was only one crash in 3 million miles driven, this is evidence for values of P near 1/(3 million), and evidence against values of P far from it.
Is it strong evidence? Nope! But it's evidence!