# Shortcuts With Chained Probabilities

post by jefftk (jkaufman) · 2021-02-18T02:00:05.283Z · LW · GW · 6 commentsLet's say you're considering an activity with a risk of death of one in a million. If you do it twice, is your risk two in a million?

Technically, it's just under:

1 - (1 - 1/1,000,000)^2 = ~2/1,000,001This is quite close! Approximating

`1 - (1-p)^2`

as
`p*2`

was only off by 0.00005%.
On the other hand, say you roll a die twice looking for a 1:

1 - (1 - 1/6)^2 = ~31%The approximation would have given:

1/6 * 2 = ~33%Which is off by 8%. And if we flip a coin looking for a tails:

1/2 * 2 = 100%Which is clearly wrong since you could get heads twice in a row.

It seems like this shortcut is better for small probabilities; why?

If something has probability `p`

, then the chance of it
happening at least once in two independent tries is:

1 - (1-p)^2 = 1 - (1 - 2p + p^2) = 1 - 1 + 2p - p^2 = 2p - p^2If

`p`

is very small, then
`p^2`

is negligible,
and
`2p`

is only a very slight overestimate. As it gets
larger, however, skipping it becomes more of a problem.
This is the calculation that people do when adding micromorts: you can't die from the same thing multiple times, but your chance of death stays low enough that the inaccuracy of naively combining these probabilities is much smaller than the margin of error on our estimates.

*Comment via: facebook*

## 6 comments

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## ↑ comment by Measure · 2021-02-18T11:28:28.999Z · LW(p) · GW(p)

The naive approximation gives 100% chance of death for both options, but we know it's less accurate for larger probabilities, so that should mean the two 50% risks is safer. In fact, 1 - (1 - 0.5)^2 = 75% is actually larger than 1 - (1 - 0.05)^20 = 64%. This means that the naive approximation is also bad at numerous iterations (large exponents).

## ↑ comment by jefftk (jkaufman) · 2021-02-18T19:35:45.652Z · LW(p) · GW(p)

With such a high chance of death I'm not using any approximations!

```
>>> (1-1/20)**20
0.36
>>> (1-1/2)**2
0.25
```

Replies from: None## ↑ comment by just_browsing · 2021-02-18T21:46:04.299Z · LW(p) · GW(p)

The intuitive way to think about this is the heuristic "small numbers produce more extreme outcomes". Both choices have the same expected number of deaths. But the 50% option is higher variance than the 5% option. Our goal is to maximize the likelihood of getting the "0 deaths" outcome, which is an extreme outcome relative to the mean. So we can conclude the 50% option is better without doing any math.

Replies from: philh## ↑ comment by philh · 2021-02-22T12:33:49.182Z · LW(p) · GW(p)

You got the wrong answer, but I do like the idea of comparing variances, and at least for this distribution, whichever has greater variance will have more weight on 0. But in this case, the variance of the 50% option is 0.5 and the variance of the 5% option is 0.95. And indeed the 5% option is preferable. ( has variance , if the means are the same then whichever has lower will have higher variance.)

Replies from: just_browsing## ↑ comment by just_browsing · 2021-02-22T15:54:27.778Z · LW(p) · GW(p)

That'll teach me to post without thinking! Yes, you're right that is the better way to deal with variance here. (Or honestly, the method from the above comment is the slickest way.)

I had been thinking of a similar kind of situation, where you have a fixed and varying sample sizes . Then, the smaller gives more extreme outcomes than larger . Of course, this isn't applicable here.