It is
not a misuse of statistics for "one data point" to significantly shift our beliefs. Let's do the math.
Bayesian approach:
To make the math really simple, let's assume a discrete prior on Uber's death rate. Say 33% that Uber's cars are much safer than humans (0.1 deaths per 100M miles), 33% that they are equally safe (1 death per 100M miles), and 33% that they are much more dangerous (10 deaths per 100M miles). After observing one death at 3 million miles, your posterior is should update to {safer: 1%, equal: 11%, more dangerous: 88%). This is a substantial shift in confidence.
Math: http://www.wolframalpha.com/input/?i=(1-10%2F100)%5E2*(10%2F...)
Frequentist approach:
Let the null hypothesis be that Uber's self-driving cars have the same death rate as humans - 1 death per 100 million miles. The odds of Uber killing someone within 3 million miles is about 3%. Therefore, we can reject the null hypothesis with a p value of 0.03. One positive "data point" is statistically significant.
Statistically, one death after 3 million miles is not proof that Uber's death rate is higher than 1 in 100 million miles. But it is statistically significant, in both a frequentist and Bayesian framework. You have to get really, really unlucky to have a death at 3 million miles if your death rate is 1 per 100 million miles.
Bottom line: This collision isn't proof, but it's strong evidence. (To go along with all the evidence from crash rates, disengagement rates, engineers working at these companies, and the video of the crash itself.)