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Comment by nickhuh | Hacker News Reader

I think this example misleads one's intuition for the following reason: in the proposed scenario, you'd only see the coin come up heads 10 times in a row about 1 in 1024 times you ran the experiment. While your conclusion would likely be incorrect, you almost never run into that scenario.

For example, if you conducted a study every week for 20 years, you'd both be extremely prolific and expect to have drawn about one wrong conclusion.

The example is a case of an absurd premise (i.e., a fair coin comes up heads 10 times in a row) leading to an absurd conclusion (that the coin is biased). Of course, this is exactly the guarantee the hypothesis test provides: under robust assumptions, you'll draw the wrong conclusion only rarely.

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A 1 in 1024 chance is not an absurd premise! Something that has a 1 in 1024 chance of ocurring to a person happens to 7 million people. If you get that evidence, you need to be capable of interpreting it correctly.

The OP's example would also worked well enough with even just 5 heads; you'd already pass p < 0.05, despite the actual probability of having picked the biased coin still being minuscule.

You're actually being misled. Formally, you have an observed event, 10H and so you want to know the probability that your coin was fair, conditioned on having seen 10 heads. You have fair coins (F) and biased coins (B)

You have P(H) == 1/1024, P(H|B) == 1/1. And,

Then the probability that you picked a biased coin (P(B))? 1/10^1000. The probability you picked a fair coin (P(F))? (1 - 1/10^1000)

All in all, P(B|H)? Well Bayes rule tells us P(A|B) = P(B|A) * P(A) / P(B). Or in this case, P(B|H) = 1 * 1024 / 10^1000, or approximately 1 / 10 ^ 997.

> For example, if you conducted a study every week for 20 years, you'd both be extremely prolific and expect to have drawn about one wrong conclusion.

This is a prior! In the kind of experimentation I do, I run literally tens of thousands of experiments a day.

I've been playing a board game daily for the last two weeks. As part of this game, a player has to draw one out of six unique cards. In the last five games, I've repeatedly drawn the same card. This is a real example.

1 / (1/6)^5 ≃ 7776

You can't rely on odds after an event has happened to determine probably. I could shuffle a deck containing 1000 unique cards and then look at their order. It's astronomically low that this order occurs but it did happen.

When discussing conditional probability, this is absolutely what you can do.

Let's use your example. Conditional probability is P(A|B), the probability of event A, given that event B was observed. What's the probability that I am a magician, given that I shuffled the deck and when you saw it it was still in new deck order?

Now certainly, there is an astronomically small chance that this was observed due to random chance. And yet if you observed this, I'd expect that you would, with relatively high confidence, believe that I stacked the deck.

I'm not doing anything different than what we are doing when discussing the improbability of a hypothetical 10-heads-in-a-row event.

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