- 20
- -2

#### What is this place?

This website is a place for people who want to move past shady thinking and test their ideas in a
court of people who don't all share the same biases. Our goal is to
optimize for light, not heat; this is a group effort, and all commentators are asked to do their part.

The weekly Culture War threads host the most
controversial topics and are the most visible aspect of The Motte. However, many other topics are
appropriate here. We encourage people to post anything related to science, politics, or philosophy;
if in doubt, post!

Check out The Vault for an archive of old quality posts.
You are encouraged to crosspost these elsewhere.

#### Why are you called The Motte?

A motte is a stone keep on a raised earthwork common in early medieval fortifications. More pertinently,
it's an element in a rhetorical move called a "Motte-and-Bailey",
originally identified by
philosopher Nicholas Shackel. It describes the tendency in discourse for people to move from a controversial
but high value claim to a defensible but less exciting one upon any resistance to the former. He likens
this to the medieval fortification, where a desirable land (the bailey) is abandoned when in danger for
the more easily defended motte. In Shackel's words, "The Motte represents the defensible but undesired
propositions to which one retreats when hard pressed."

On The Motte, always attempt to remain inside your defensible territory, even if you are not being pressed.

#### New post guidelines

If you're posting something that isn't related to the culture war, we encourage you to post a thread for it.
A submission statement is **highly appreciated**, but isn't necessary for text posts or links to largely-text posts
such as blogs or news articles; if we're unsure of the value of your post, we might remove it until you add a
submission statement. A submission statement is required for non-text sources (videos, podcasts, images).

Culture war posts go in the culture war thread; all links must either include a submission statement or
significant commentary. Bare links without those will be removed.

If in doubt, please post it!

#### Rules

- Courtesy
- Content
- Engagement
- When disagreeing with someone, state your objections explicitly.
- Proactively provide evidence in proportion to how partisan and inflammatory your claim might be.
- Accept temporary bans as a time-out, and don't attempt to rejoin the conversation until it's lifted.
- Don't attempt to build consensus or enforce ideological conformity.
- Write like everyone is reading and you want them to be included in the discussion.

- The Wildcard Rule
- The Metarule

## Jump in the discussion.

No email address required.

## Notes -

AuthorNoteTagActionsThe level N skeptic achieves true enlightment by admitting that all he knows is that he knows nothing.

## More options

Context Copy link

So then is the level 3 skeptic the one who points out that the answer to the coin problem depends on your prior? The answer given, where the probability of success follows Beta(successes, trials), is more rigorously derived by taking Beta(1,1) as the prior (which is the same as the uniform distribution); see mathematical details at https://en.wikipedia.org/wiki/Rule_of_succession. The upshot is that if you have seen many coins before, and they resemble the coin you have right now, then that is evidence that the probability of heads is similar to the probability of heads for those other coins, so the prior is different.

Yes, but to point out what the true answer depends on, a level-3 skeptic has to first

doubtthe problem. A level-3 skeptic might not know the answer, but it's better to say "I don't know", than what some confident people would automatically say: 50%.It's reasonable to express uncertainty, but for a case like this with a very limited set of possible outcomes then "I don't know" should still convert to a number. In fact, with maximum uncertainty, 50%

iscorrect: If your distribution over the true probabilities is uniform, then integrating over that distribution gives your subjective probability of heads as 1/2. On the other hand, if you've flipped a lot of coins and you know that most coins are fair, then seeing 8 heads shouldn't move the needle much, so the answer might not be exactly 50% but it would be quite close.No, it's a

function, not a single number.No, if it's a uniform distribution you can calculate the probability that the actual probability is between 45% and 55%: 10%. For me 10% is

very unlikely.But the probability that the actual probability is between 90% and 100% is equally likely: 10%.

You are confusing the most likely probability with "the answer". The most likely probability is close to 50%, yeah, but that's not the answer. The answer is a

function. Given that function you can calculate the probability that the actual probability is between 45% and 55%, and given that the most likely probability is in this range, the likelihood is going to be high, but there's a non-zero probability that the true probability lies outside that range.Probabilities of probabilities should make anyone question their own certainty on "the answer".

If you have a distribution over a probability of an outcome, it's entirely valid to integrate over that density and get a single number for the probability of the outcome. This is done all the time in probability. In fact, this works for any parameter: If you have a probability distribution Y for the mean of a random variable X with standard deviation 1, for example, then you can compute the average value of X. Specifically, the average of each of the possible normal distributions, weighted by how likely that distribution is according to Y. (The exact interpretation of what this process means depends on your interpretation of probability; for the first case, a frequentist would say something about flipping many coins, where the probability of heads for each is selected from the distribution, while a Bayesian would say something about your subjective belief. But the validity of this process can be confirmed by doing some calculus, or by running simulations if you're better at programming than math).

You get the probability that the actual probability is on that region, but it's never 100%.

But the average value is not necessarily "the answer".

I have no idea what you're trying to say here. If you have a distribution for the probability of heads, you can calculate the probability of getting heads. For any symmetrical distribution, it will be 50%, reflecting the fact that you have no reason to favor heads over tails.

Think about it this way: Suppose that you have a much simpler distribution over p, the probability of heads, where it's 0.4 with probability 0.3, otherwise 0.7 Then by the law of total probability, the probability of heads is (probability of heads given p=0.4)*(probability p = 0.4) + (probability of heads given p = 0.7) * (probability p = 0.7) which is clearly 0.12 + 0.49 = 0.61. You might note this is also the expected value of p; in the continuous case, we would use the formula integral_0^1 xf(x) dx where f is the PDF. For your solution, Beta(9, 3), this is just 9/12 = 0.75. This is basically the same example as at the top of https://en.wikipedia.org/wiki/Law_of_total_expectation#Example

I never said it was? It was just another example where you can compute a specific property of the underlying random variable, given a distribution on one of its parameters.

Actually you can't. I don't think you quite understand the point. I can program a

`f()`

function that return heads`p`

percent of the time. How many results do you need to accurately "calculate the probability of getting heads"?OK.

Yes, but the "expected value" is not "the answer".

I programmed your example of

`0.3*0.4/0.7*0.7`

as`g(0.3)`

, let's say that the threshold`t`

in this case is 0.3, but I choose a different threshold for comparison and I run the function 10 times. Can you guess which results are which?`[0.7, 0.4, 0.7, 0.7, 0.7, 0.7, 0.4, 0.7, 0.7, 0.4]`

`[0.7, 0.7, 0.7, 0.7, 0.4, 0.7, 0.7, 0.4, 0.7, 0.7]`

Which is

`g(0.3)`

, which is`g(t)`

, and what do you guess is the value of`t`

I choose?What is "accurately"? The method I described will give the correct probability given all of the information available. As you gather more information, the probability changes. Are you getting confused between probability and statistics?

Yes, you can. I just gave you a complete worked example.

In this case, it is. In fact, in any case where you have binary outcome and independent events, the expected number of successes is equal to the p*the number of trials. In the special case of n=1 coin flip, we have E(number of heads) = p. See https://en.wikipedia.org/wiki/Binomial_distribution

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

Thanks for writing this. I found it accessible, despite being fairly weak on stats (though I do remember what a beta distribution is).

Your piece has a vibe of a warning for young rationalists that goes something like, "Beware, for not all who claim to be skeptics are ones." Would you say this is a correct interpretation?

Yes. Many people who claim to be skeptics actually are being skeptical in many claims, but the point of calling yourself "skeptic" is that you are being skeptical in

allof them (or close to 100%). You can't call yourself a "peaceful" person if there are enough times you've reacted violently.## More options

Context Copy link

## More options

Context Copy link

Very good, although I would quibble with the wording in a few places, e.g. -

"A meta-skeptic should doubt everything"

I would put it as Hume did when discussing miracles: "A wise man proportions his belief to his evidence." Evidence is never conclusive, but it can be stronger or weaker. The coin toss observations favour the hypothesis that the coin is biased towards heads, but not to an extent that can't be easily dismissed as random error.

Professional skeptics tend to focus on easy cases where credulity goes wrong, which encourages the conflation of skepticism and denial that you describe.

Indeed. This is a point I often emphasize in debates. The quote "absence of evidence is not evidence of absence" is wrong because it is evidence, but people often confuse evidence with proof.

But I don't see evidence as a continuum, I see certainty as a continuum. I would say for example "I believe the coin is biased with 95% certainty". 50% certainty means no belief one way or the other. This is a matter of semantics of course.

In the end what "true skeptics" should agree is that 100% certainty is not characteristic of skepticism.

Yes, and some Bayesians would even distinguish between e.g. 50% certainty in the coin landing heads on the next toss after 50 heads and 50 tails from your rational beliefs before testing the coin at all. They would model the latter with a convex set of different beta distribution priors (some very biased to heads, some very biased to tails) and the former as the beta posteriors after using your observations of the 100 coin tosses to do Bayesian updating on each element in that set. I'm not persuaded by this "Imprecise Bayesianism," but I agree that it's a useful distinction.

https://plato.stanford.edu/entries/imprecise-probabilities/

You can use the beta distribution to calculate the probability that the actual probability is between 45% and 55% given 50H/50T, and it's around 70%: graph. So in that case I would say I believe the coin is fair with 70% certainty. With 0H/0T it's around 10%.

The more tosses the more likely the actual probability is between a certain range, so the more "precise" it should be.

Articles from Stanford Encyclopedia of Philosophy are very interesting, but way too complicated for me. This article is no exception, very interesting, but my point is much more general.

By using probability I'm not trying to find an accurate value of belief, what I'm trying to do is show is that even in simple questions people have an unwarranted level of certainty, even people who call themselves "skeptics".

Sorry, wasn't meant as a critique: just something else that is interesting to think about.

Yes. I didn't consider it a critique. I think we are talking about the same thing except at different levels, like those Wired videos of explaining one concept "in 5 levels of difficulty".

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link

## More options

Context Copy link