Erm... I don’t think statistics work like this. You can‘t go and pick a confidence level that „confirms“ your desired outcome.
People with more knowledge about statistics than me might be able to explain why.
People with more knowledge about statistics than me might be able to explain why.
But the poster did something different. He took it one level further and attempted to calculate this confidence number for different spans in which the actual "deaths per 100 million miles" number of Uber's current cars would fall into, given an ideal world (from a data perspective) in which they would have driven an infinite amount of miles. But he actually did it the other way round - he modified the confidence variable and calculated the spans, and then he adjusted the confidence until he arrived at a span that would put Uber's cars just on par with human driving in the best case.
The fact that a fatal incident happens that early (at 3 million, and not closer or past the 86 million that a statistical human drives on average until a fatal incident occurs) does not allow us to extrapolate a sound number per 100 million miles, but it tells us something about the probability by which the actual number of fatalities by 100 million miles that we'd get if Uber continued testing just like it did and racked up enough miles (and killed people) for a statistically sound calculation will fall into different margins. Sure, Uber could have been just very, very unlucky - but that's pretty unlikely, and the unlikeliness of Uber's bad luck (and conversely the likeliness of the fact that Uber's tech is just systematically deadly) is precisely what can be calculated with this single incident.
The assumption of using Poisson seems pretty solid to me, given we are talking about x events in some continuum (miles traveled in this case), but always happy to hear any cogent objections.
The bigger problem though is what you are doing with your confidence interval. A CI is a statement about replication. A 95% confidence level means that in 100 replications of the experiment using similar data, 5 of the generated CIs -- which will all have different endpoints -- will _not_ contain the population parameter, although IIRC this math is more complicated in practice, meaning that the error rate is actually higher. As such, if you generate a CI and multiply the endpoints by some constant, that's a complete violation of what is being expressed: there is vastly more data with 100m driving miles than 3m miles, which will cause the CI to shrink and the estimate of the parameter to become more accurate. There is absolutely no basis for multiplying the endpoints of a CI!
Ultimately, given that the size of the sample has an effect on CI width, you need to conduct an appropriate statistical test to compare the estimated parameters between the 1 in 3m deaths for Uber and whatever data generated the 1.18 in 100m deaths for sober drivers. There's a lot more that needs to be taken into account here than what a simple Poisson test can do.
For an analysis of how AVs with various safety levels perform in terms of lives saved over time, I recommend https://www.rand.org/blog/articles/2017/11/why-waiting-for-p...
Edit: Note the default values of the T and r parameters when you run poisson.test(1, conf.level = 0.95), and also that the p-value of the one-sample exact test you performed is 1. Also, since this is an exact test, the rate of rejecting true null hypotheses at 0.95 is 0.05, but given my reservations about the use of a Poisson distribution here, I don't think that using an exact Poisson test is appropriate.
Null hypothesis: The true rate of events is 1 (r) with a time base of 1 (T).
Alternative hypothesis: The true rate is not equal to 1.
The reason that you end up with a p-value of 1 is because you've said that you've observed 1 event in a time base of 1 with a hypothesized rate of 1. So given this data, of course the probability of observing a rate equal to or more extreme than 1 is 1! As such, you're not actually testing anything about the data that you claim you are testing.
I'm not trying to be harsh here, but please be careful when using statistics!
> poisson.test(c(1, 11800), c(3, 1000000), alternative = c("two.sided"),conf.level = .93)
Comparison of Poisson rates
data: c(1, 11800) time base: c(3, 1e+06)
count1 = 1, expected count1 = 0.035403, p-value = 0.03478
alternative hypothesis: true rate ratio is not equal to 1
93 percent confidence interval:
1.006334 146.142032
sample estimates:
rate ratio
28.24859
Interestingly, if you multiply the CI I claimed before by the rate ratio instead of the expected rate, you get almost exactly the same CI as here.
> ci <- c(0.03562718, 5.17251332)
> 28.24859 * ci
[1] 1.006418 146.116208