## Archive for the ‘Uncategorized’ Category

### Why You Keep Seeing Me

October 1, 2013

Yesterday afternoon I ran into a friend, by chance, for the third time that day.

“Why do I keep seeing you?” he asked.

“Good question,” I said, “you see, about eight light-minutes in that direction is a giant ball of gas called the sun. Inside, hydrogen under high pressure and temperature fuses into Helium. The Helium nucleus is lighter, so by E=mc^2 energy is given off in the form of photons…” and went on discussing atmospheric scattering, optics, the physiology of the retina, nerve impulses, a hierarchy of vision-processing mechanisms in the brain, the fusiform gyrus, grandfather neurons, and the nature of consciousness, all contributing to why he saw me.

But I was a bit surprised, as I dragged the joke on for a few minutes, how often I didn’t really understand what I was saying (and said it rather poorly). Hydrogen fuses to form Helium in the sun? Why does it do that? I thought I knew most of what there is to know about a single Hydrogen atom from my quantum mechanics courses, but put two of them together (and give the nucleus actual degrees of freedom) and i have no idea why they do what they do.

Today I ran across this quote from Tom Stoppard’s Arcadia:

It makes me so happy. To be at the beginning again, knowing almost nothing…. The ordinary-sized stuff which is our lives, the things people write poetry about—clouds—daffodils—waterfalls….these things are full of mystery, as mysterious to us as the heavens were to the Greeks…It’s the best possible time to be alive, when almost everything you thought you knew is wrong.

(quoted in Melanie Mitchell’s Complexity: A Guided Tour)

There is a lot of mystery in something as mundane as seeing a face you recognize. Fortunately, today many of the answers to these mysteries are known, if not in full detail, then at least in much greater detail than I know now. It’ll be interesting to find a few of these things out.

### Simple experiments with beats and hearing

September 29, 2013

In the class I’m TAing, we are discussing beats. Beats are when you play two tones that are close to each other, but at slightly different frequencies. They will slowly drift from in phase to out of phase, like when your turn signal has slightly different timing from that of the car in front of you at a stop light. In sound, this results in the “wa-wa-wa” sound you hear when tuning two instruments to each other.

Beats depend on space as well as time. If two speakers play a tone, they can be in phase at one place and out of phase at another, since the distances to the speakers is different.  I had never actually played around with this with sound before, so I set up two speakers in a room where I could move both them and myself around pretty freely.

Making the speakers play the same pure tone (440 Hz), I found it was quite easy to observe the interference pattern of the speakers by moving my head around. And when the speakers were placed far enough apart, the phase difference could be quite different at opposite ears, so the sound would appear only in my left or only in my right ear. Also, by putting the speakers the right distance apart, I was able to observe turning the volume on the closer speaker up from zero and hearing the volume of the sound go down as the front speaker interferes with the back one. (It’s not practicable to make the sound seem to disappear entirely.)

Next, playing notes separated by 0.5 Hz, I found I could stand in one spot and hear the sound move from my left ear to my right ear and back again once every two seconds. This never sounded as if the source itself was moving around. Instead, the sound felt as if it was being played right next to my ear, even though the speakers were a couple meters in front of me.

These are just simple things, but still striking, since we rarely encounter coherent sound sources set up this way in daily life. Some time ago I did one other experiment accidentally that had an interesting result. I was listening to an app that creates beats by outputting different frequencies to the left and right audio channels, but I didn’t hear the beats. After a bit of confusion, I realized it was because I was wearing earbuds – there was no interference because nobody was getting both signals at once. Playing the tones through speakers instead, I heard the beats. However, when I turned the frequency down, I could hear the beats even with the earbuds. Somehow, your brain collects phase information about sound, but only if the sound is slow enough. The transition occurred somewhere around 600 Hz, or roughly a millisecond. This corresponds fairly well to the width of a nerve impulse, so a likely hypothesis is that you can only determine the arrival of the peak or trough of a wave to a time comparable to the width of a nerve impulse, which means you only have meaningful phase information down to around the 600 Hz I heard. (In a discrete Fourier transform, you have information about frequencies up to half the inverse of your sampling rate, so this checks out pretty well.)

The app I used was similar to this one, but I think my cheap headphones don’t process audio channels properly, as I can hear the beats even with one earbud in, both with that app and with WIkipedia’s file.

### Answer: weights on a pendulum

September 25, 2013

The problem asked why weights are added or subtracted on top a pendulum, and also how much friction is needed to keep the weights from falling off.

The weights are added on top the pendulum to make it go faster when it’s too slow. Because the weights are above the pendulum’s center of mass, they raise the center of mass slightly, reducing the period.

One commenter suggested their purpose was to slow the pendulum by stretching it out, thus lowering the center of mass. The Young’s modulus of wood is roughly 10^10 N/m^2. The cross-section of that pendulum might be 5*10^-4 m^2, giving a characteristic force of 5*10^6 N. The weights are small, maybe 1N total, while the length of the pendulum is about 1m, so the stretch of the pendulum is only about 2*10^-6m.

The mass of the pendulum might be a few hundred times the mass of the weights, and they’re perhaps 5-10cm above the pendulum’s center of mass. So they raise the center of mass of the pendulum by about maybe .05cm, dwarfing the contribution from the stretching of the pendulum. The pendulum’s center of mass is moved by .05 percent, which changes the frequency by half as much (because frequency depends on length^(-1/2)). That’s a few parts in ten thousand, or around 10 seconds a day.

The weights are very unlikely to slip off, even if the friction is pretty low. The reason is that the tangential force on them (which comes from friction) is very small. They have nearly the same motion as the pendulum itself, and the non-gravitational force on the pendulum is directed purely radially.

When the pendulum is at an angle A from vertical, it experiences a tangential gravitational acceleration g sin(A). The weights feel the same gravity, but their actual tangential acceleration is only about 90-95% as large (based on being only 90-90% the distance from the pivot as the pendulum’s center of mass). So the weights experience a friction force of roughly .1 g sin(A) m.

At the peak of their oscillation, the normal force is g cos(A) m, and friction is limited to mu g cos(A) m. Thus, they will only slip if .1 sin(A) > cos(A) mu, or mu < .1 tan(A).

### Not as interesting as I thought

September 19, 2013

As often happens, after writing yesterday’s post, it stuck around in the back of my mind, and I think I might be wrong about it.

Briefly, the background is that a friend was looking at lopsided results from a supposedly-random survey, and wondering whether they were really random. People warned her to be careful about her judgment because unlikely things are likely to happen. My conclusion was that we should estimate whether the survey was biased using Bayes’ formula, and I didn’t see a  place there for the “unlikely things are likely to happen” heuristic. I wrote

But “human biases” doesn’t seem to have any obvious spot in Bayes’ formula. The calculation gives a probability that doesn’t have anything to do with your biases except insofar as they affect your priors. Who cares whether the program has been used hundreds or thousands of times before? We’re only interested in this instance of it, and we don’t have any data on those hundreds or thousands of times.

and went on to say

In the end, the “unlikely events are likely to occur” argument doesn’t seem relevant here. If we looked at a large pool of surveys, found one with lopsided results, and said, “Aha! Look how lopsided these are! Must be something wrong with the survey process!” that would be an error, because by picking one special survey out of thousands based on what its data says, we’ve changed $P(data|random)$. That is, it is likely that the most-extreme result of a fair survey process looks unfair. But we didn’t do that here, so why all the admonitions?

But since then, I’m unsure about my statement, “the most-extreme result of a fair survey process looks unfair. But we didn’t do that here”. It’s true that there is only one survey in question, and we aren’t picking the most extreme survey out of many. However, we are picking one particular thing that happened to someone out of many. And this can, I think, come into Bayes’ formula.

A good Bayesian must use every bit of available evidence in their calculations. The survey results are one piece of evidence, but another is the fact that we decided to analyze this survey. If the survey had come up with a fair-looking split, like 80 – 92 – 88, no one would have stopped to think about it. But we did stop to think about it. So Bayes’ formula should not be

$P(random|data) = \frac{P(data | random)P(random)}{P(data)}$

but

$P(random|data + special\_notice) = \frac{P(data + special\_notice|random)P(random)}{P(data+special\_notice)}$

If you have a large pile of surveys and you pick one up at random intended to see whether it looks fair, then notice that its results look wonky, you are perfectly justified in saying there might be something wrong with the survey process. If you have a large pile of surveys and you root through them until you find one that’s wonky, you aren’t. The key isn’t the number of surveys that exist, since that’s the same in both cases. It’s that in the second case, the fact that you took notice of the survey changes the likelihood of seeing skewed results, and this must be taken into account. So at least preliminarily, until I change my mind again or get some more-expert feedback, I eat my words on what I said yesterday.

### How interesting is that license plate?

September 19, 2013

You know, the most amazing thing happened to me tonight. I was coming here, on the way to the lecture, and I came in through the parking lot. And you won’t believe what happened. I saw a car with the license plate ARW 357. Can you imagine? Of all the millions of license plates in the state, what was the chance that I would see that particular one tonight? Amazing!

-Richard Feynman (source)

A friend on Facebook commissioned a survey with three conditions, which were to be assigned randomly to participants (uniform distribution). The number of responses was

condition A: 58

condition B: 94

condition C: 108

total: 260

Seeing the lopsided result, she called the survey company to ask what was up. The representative said it was just random chance. How should one react? What sort of reasoning is useful here, and what is not? Is something strange going on with the survey?

If you crunch through some quick math, you’ll see that if the survey is fair, the odds of getting a result as extreme as 58/260 via random chance are a bit less than one in a thousand. (I’m accounting for over- or under-representation in any of the three categories.) How meaningful is that?

Suppose you are walking home and find a 20-dollar bill. The odds of that might be about 1/1000, but you likely don’t think anything fishy is going on. You chock it up to good luck and pocket the bill. But next suppose you remember a time when you found a 20-dollar bill when you were walking home as a little kid, and you realize you found it right outside your grandparents’ house (which you pass on the way), and they happened to be watching from the window when it happened. These circumstances don’t change the probability of finding a 20-dollar bill by random chance, but they change our estimate of the probability that finding the bill was a fluke. How meaningful an unlikely result is depends on not only how unlikely it is, but also on the plausibility of competing alternatives.

This is captured in Bayes formula. For the survey, it is

$P(random|data) = \frac{P(data | random)P(random)}{P(data)}$

where $P(random|data)$ is the probability the survey process was a fair, uniform, random one given the observed 58-94-108 split. $P(data|random)$ is the probability of observing our results given a fair random process. $P(random)$ is the prior probability we assign to the process being a fair random one, and $P(data)$ is the overall chance of seeing our 58-94-108 split under any circumstances, including unfair ones.

The easy one is $P(data|random)$. It comes to $10^{-6}$ (see calculation here).

Discussion of the issue, then, ought to focus on estimates for $P(random)$ and $P(data)$. In part, it did

Are you counting people as they start the survey or as they finish? Because if it’s the latter, and option A is more work than the other two…

(suggests $P(random)$ isn’t very high due to varying attrition rates, and that $P(data)$ isn’t very low because varying-attrition could cause the observed bias.)

mostly I assume that it is random because randomness is pretty easy to code

($P(random)$ is high)

Actually, what you should be calculating is the Bayes factor, given the observed data, of a uniform distribution vs. a categorical distribution with a Dirichlet prior.

(Focuses on $P(data)$. However, it suggests a slightly-different metric to look at than estimating the probability that the survey process is fair. Seems like a good suggestion, but it’s not my main point here.)

How does one calculate the probability that Qualtrics would make a mistake?

(focusing on $P(random)$)

I believe that if something went wrong with that kind of coding, the outcome would look very different (like it would skip one group altogether)

($P(data)$ is low)

One thing everyone should keep in mind is that the alternative hypothesis here is NOT “their random number generator is broken.” (I mean, that’s possible, but it’s not on my list of top ten likeliest alternative hypotheses).

The alternative hypotheses here are things like “I misunderstood how to use Qualtrics ‘randomizer’ function.” Or, “Qualtrics intentionally assigns lower probabilities to longer test conditions.” Or “There’s a higher dropout rate in this test condition.” (Although I *think* that last hypothesis has been falsified by now.)

(Suggests why $P(random)$ isn’t necessarily so high, and why $P(data)$ is significant.)

Ultimately, the estimate you generate will be subjective, i.e. based on your priors and your assumptions about how to model the survey process. That’s why we see people using a lot of heuristic reasoning about the calculation – heuristics are how we deal with subjective estimates.

But in addition to discussion aimed at estimating the components that go into the Bayesian calculation, there was an entirely different type of heuristic reasoning, one focusing on human biases

I think Kahneman and Tversky did research on this? On coin flips people think HTHTTHT is more likely than HHHHHHH because it “looks more random.” Both are equally likely.

The funny thing about this (related to what A—- was saying) is that we’ve got research which shows just how difficult it is for human beings to accept randomness when they see it.

although a chi-square test shows that it’s a highly unlikely result, anything is possible, and it would be unusually NOT to see some unlikely results.

The statistical calculation only gives us the probability that such an outcome would occur. It doesn’t rid us of our preconceived notion that it should not occur, nor does it remind us that even low probability events occur quite often.

What I’m saying is that a program that has been used hundreds (or perhaps thousands?) of times to randomize subjects into conditions will often produce an outcome in the tails of the distribution.

The idea seems to be that one shouldn’t be alarmed just because your model says an event of low probability occurred. Even if your models of the world are in general correct, so many things happen that you’ll observe rare events once in a while. Further, we’re biased to make a big deal out of things, thinking they’re not random when they are. This bias in what we notice is the basis for Feynman’s joke – no one ever points out every mundane thing that occurs; only the few that seem surprising to them. Most, they don’t notice.

But “human biases” doesn’t seem to have any obvious spot in Bayes’ formula. The calculation gives a probability that doesn’t have anything to do with your biases except insofar as they affect your priors. Who cares whether the program has been used hundreds or thousands of times before? We’re only interested in this instance of it, and we don’t have any data on those hundreds or thousands of times. The only extent to which that matters is that if the program has been used many times before, it’s more likely that they’ve caught any bugs or common user errors.

In the end, the “unlikely events are likely to occur” argument doesn’t seem relevant here. If we looked at a large pool of surveys, found one with lopsided results, and said, “Aha! Look how lopsided these are! Must be something wrong with the survey process!” that would be an error, because by picking one special survey out of thousands based on what its data says, we’ve changed $P(data|random)$. That is, it is likely that the most-extreme result of a fair survey process looks unfair. But we didn’t do that here, so why all the admonitions?

Another point made by commenters was that HTHTTHT is equally-likely with HHHHHHH given a fair coin, but only the second one raises an eyebrow. This is because HTHTTHT is one of a set of a great many similar sequences while HHHHHHH is unique. But this doesn’t seem relevant here, either. We didn’t look at the exact sequence of responses 260 (BBCABACCABAC…) and claim it was unlikely. All sequences are equally unlikely given a fair random process. But instead we looked at a computed statistic – the distribution of A, B, and C, which captures most of what we’re interested in. So again, why did commenters bring this up?

Maybe I’m missing an important point, but my guess is that it’s just pattern matching. “Oh, someone is talking about an unlikely thing that happened. Better warn them about Feynman’s license plate.” Of course, we do pattern-matching all the time because it usually works. But we also need to get feedback whenever our pattern-matching fails, then try to figure out why it failed, then try to update the pattern-matching software to work better next time, gradually giving fewer false positives and more true positives. There’s a tradeoff between them, and I’d guess it’s better to err on the side of committing false positives, since you can go build a general skill of going back and checking over what you’ve said carefully after initially pattern-matching it, especially in writing.

### Blogging Update

September 18, 2013

For a long time I’ve updated this blog at best sporadically, often holding back from writing because I thought something wasn’t interesting or noteworthy enough, or that I wouldn’t write well enough or think clearly enough. Perfectionism is crippling; it’s not only the enemy of productivity, it probably lowers the quality of your stuff in the long run. So I intend to write more posts, and to do it by maintaining a low standard. Just a warning.

### People Hearing Without Listening

August 21, 2013

I’ve seen several links and discussions today to this paper about judging classical music competitions.

The experimenter had people observe clips of musicians in competitions, then guess how well the musicians placed. Subjects guessed better when given video-only clips as compared to audio clips or audio+video clips. Conclusion: people care about looks far more than they think or admit they do.

But I think we can’t jump to such a conclusion based on this paper for a few reasons.

First, the clips were taken from the top three places at prestigious international competitions. These people are already the very best; there was probably very little variation between them. If we rate the auditory quality of the music they played out of 100, maybe they’re at 94, 95, and 96, or something. It’s not surprising that experts didn’t accurately judge who would win based on sound.

The failure of audio clips to predict competition placement is similar to how SAT scores aren’t very good predictors of the performance of Caltech students. If you took randomly-selected students from everyone applying to college and admitted them to Caltech, SAT score would be an excellent predictor of their success. But Caltech only admits people with very high SAT scores to begin with, so there’s not that much variation available to do the predicting.

Meanwhile, the variation in how the musicians move and express themselves physically could potentially be large – 50, 70, 90, for example. So even if judges base their scores mostly on the quality of playing, the visual aspect can still dominate the final rankings. The data don’t support the author’s claim “the findings demonstrate that people actually depend primarily on visual information when making judgments about music performance.” To show that, you’d need to show that visual information still trumps auditory information even when the players are not at about the same level. And it’s not like people with visual information did very well – they got to roughly 50% accurate. If you go from a distribution of 1/3 -1/3-1/3 to 1/2-1/4-1/4 you’ve reduced your entropy by about five percent.

Additionally, the clips used in this paper were six seconds long. So what we’ve shown is that you get a better quick, gut-instinct impression with visual than with auditory, but this doesn’t say a whole lot about the judges who were watching and listening to the entire performance.  (Edit: as a commenter pointed out, the paper contains a vague description of the results holding with clips of up to one minute.)

Perhaps visual aspects of the performance are correlated with auditory aspects. Further, maybe six seconds is enough time to get a good feel for the visual aspects, but not the audio aspects (six seconds might not even be one entire phrase of the music). In that case, expert judgments during competitions could be based almost entirely on the audio aspect, but people would still predict those judgments better from videos.

It’s interesting that people were bad at predicting which choice (audio, visual, audio+visual) would give them the best results, but people have very little experience with this contrived task, so it’s not especially surprising. Further, I think the conclusions of the paper are probably true – visual impressions matter a lot in music performance, but I hold that belief based on my general model of how people work. The evidence in this paper is somewhat lacking, and it’s disappointing that a news source like NPR fails to state the important fact that the clips were not complete recordings, but very short, six-second impressions.

Elsewhere:

NPR

John Baez

Robin Hanson

### A calculator is broken so that the only

April 27, 2013

A calculator is broken so that the only keys that still work are the sin, cos, tan, arcsin, arccos, and arctan buttons. The display initially shows 0. Given any positive rational number q, show that pressing some finite sequence of buttons will yield q. Assume that the calculator does real number calculations with infinite precision. All functions are in terms of radians.

I’ve started reading Zeitz’s The Art and Craft of Problem Solving. This one took me about 90 minutes, though as usual, once I had a solution it seemed obvious. Originally from USAMO 1995. Who comes up with these problems? How? You sit down and say, “Okay, it’s time to invent a problem that can be solved with elementary math, but only if you see some diabolical trick”, and then you do what?

### Integration by parts

April 19, 2013

How did loving the ground-up toenails of bisexuals get an interior designer to take up geology? Simple, he went from noting decor to what the core denotes by being into grated bi-parts.

I don’t really get why this XKCD is funny.

But here is a picture explaining integration by parts:

The area of the entire rectangle is $uv$, and it is made of two parts we integrate, so

$uv = \int \!u\, \text{d}v + \int\! v\,\text{d}u$

and therefore

$\int \! u \,\text{d}v = uv - \int\! v \,\text{d}u$

Also, take $\text{d}(uv) = \text{d}(\int \!u \,\text{d}v + \int \!v\,\text{d}u)$ and you find

$\text{d}(uv) = u \,\text{d}v + v\,\text{d}u$,

which is the product rule.

### Bayesian Bro Balls and Monty Hall

April 13, 2013

I don’t think this is very good, but I’m tired of working on it and I said I’d write it, so here it is. See http://yudkowsky.net/rational/bayes for a better essay on thinking about probability.

A woman and a man (who are unrelated) each have two children. We know that at least one of the woman’s children is a boy and that the man’s oldest child is a boy. Can you explain why the chances that the woman has two boys do not equal the chances that the man has two boys?

(text copied from here, which attributes the problem’s popularity to an “Ask Marilyn” column in 1996)

Given a tricky problem, some people are stumped, clever people find a tricky answer, and brilliant people find a way of thinking so the problem isn’t tricky any more. This problem is about interpreting evidence, and the brilliant person who discovered how to think about evidence is Laplace. (His method is called Bayes’ Rule. Bayes came first, but Laplace seems more important based on my cursory reading of the history.) To illustrate it, let’s start with different problem, related only by the fact that it’s also about using evidence.

I picked a day of the week by first choosing randomly between weekend and weekday, then picking randomly inside that set. I wrote my chosen day down and picked a letter at random. The letter was ‘d’. What is your best guess for the day that I picked? How confident are you? (“At random” means there was uniform probability.)

First, write out all the possibilities. These are called the “hypotheses” (plural of “hypothesis”).

• Saturday
• Sunday
• Monday
• Tuesday
• Wednesday
• Thursday
• Friday

Next, find their starting probabilities, the ones we have before learning what letter was picked. Half the probabilities goes to the weekend, so they’re 1/4 each. The rest goes to the five weekdays, so they’re 1/10 each. This is called the “prior”. The picture shows lengths proportional to probability.

Let’s imagine we did this experiment 10,080 times (a number I’m choosing since it will work out well later). Then the weekends come up 2520 times each and the weekdays 1008 times each. (This is the expectation value for how many times these days would show up. In a real experiment there would be random deviations.)

Next we look at the evidence – the letter ‘d’. Out of our 10,080 experiments, how many have the letter ‘d’ result? It turns out this will happen 1565 times – 315 times on Saturday, 420 times on Sunday, etc.

The illustration looks like this (partially filled in)

The bottom bar represents all the trial where the letter ‘d’ popped up. It is too small to read, so we’ll blow it up.

We can divide by the total number of times the letter ‘d’ came up to get the probabilities. (Individually this step is called “normalizing”, but it’s really part of updating.)

We don’t really need to consider doing the experiment 10,080 times; I just thought that made it more convenient to visualize. What’s important is the probability distribution at the end. This solves the problem. We now know, given that ‘d’ was the chosen letter, the probability for each day of the week.

To recap, an outline for the procedure is

1. Find all the possibilities (i.e. define the hypotheses).
2. Determine how likely the hypotheses are beforehand (i.e. choose the prior).
3. Update the hypotheses based on how well they explain the data (i.e. multiply them by their probabilites of producing the evidence).
4. Finish updating by making the hypotheses’ probability add up to one (i.e. normalize).

Let’s reword the original problem to make it clear exactly what evidence we’re collecting, then apply the method:

There are two bros who like to tan their balls. Unfortunately, this can cause testicular cancer. Given their amount of ball-tanning, each testicle of each bro has a 50% chance of having cancer. (The testicles are all statistically independent of each other.) The two bros decide to conduct testicular exams to see whether they have cancer, and their self-administered exams are perfectly accurate. The first bro decides to examine both his testicles, then report whether or not at least one of them has cancer. The second bro decides to examine only his left testicle because he thinks that examining both would count as cradling them and be gay. Suppose the first bro reports that indeed, at least one of his balls has cancer. The second bro reports that his left ball has cancer. Do they have the same probability of having two balls with cancer?

The bros go about collecting different data. The evidence they bring to bear is different, so a good way of handling evidence should be able to show us that the probabilities are different. But there is no need to be clever about finding the solution when you have a general method at hand.

The hypotheses we’ll use are left/right both cancerous, left cancerous/right healthy, left healthy/right cancerous, both healthy. They are all equally likely.

In this case, updating is very simple. Each hypothesis explains the results either perfectly or not at all. Here is what updating looks like for the bro who tested both balls:

Here it is for the bro who tested on the left ball:

So the bro who tested both balls has a 1/3 chance of having cancer in both balls, while the bro who tested the left ball has a 1/2 chance. Both came back with a positive result, but the bro who tested both balls has weaker evidence. It is easier to satisfy “at least one ball has cancer” than to satisfy “the left ball has cancer”, so when he comes back and reports that at least one ball has cancer, he hasn’t given as much information, and his probability doesn’t shift upwards as much. A strong test is one which the hypothesis of interest passes, but which eliminates the competing hypotheses. More hypotheses pass the test “at least one ball has cancer”, so that test doesn’t eliminate as much and is not as strong.

Let’s apply the same method to the Monty Hall problem. Your hypotheses are that the prize is behind door one, door two, or door three. These are equally-likely to begin. You choose door one, and Monty Hall opens door two. That’s the evidence.

If the prize is behind door one (the door you chose), Monty Hall could have opened either door 2 or door 3, so there was a 50% chance to see the observed evidence. If the prize was behind door 2, there was 0% chance, so that hypothesis is gone. If the prize was behind door 3, Monty Hall was forced to open door two, so that hypothesis explained the evidence 100% and becomes more likely.

Here’s a diagram for the probabilities after updating:

So you should switch doors. The usefulness of this method is you don’t need a clever trick specific to the Monty Hall problem. As long as you understand how evidence works, you can solve the problem without thinking and spend your limited thinking power somewhere else.

Within this framework, we can notice some things that help make the problem more intuitive, even after it’s solved. Suppose you have a hypothesis and you collect some evidence. How much more-likely does the hypothesis become? What matters is how much better the hypothesis explains the data than the other hypotheses, not just how well it does by itself. So, if you have two hypotheses and they both explain the data equally well, that data isn’t evidence either way.

You can apply this idea to the bro who tested his left testicle. Whether the right testicle has cancer or not, the data on the left testicle is equally-well explained. Therefore, the right testicle’s probability remains unchanged from 50%, so the bro has a 50% chance of two cancerous balls.

The same is not true for the bro who tested both testicles. If that bro has cancer in his right testicle, that explains the result better than if he doesn’t. That is to say, if the bro has cancer on his right testicle, that explains the result perfectly. But if he doesn’t, there’s only a 50% chance of explaining the result. As a result, the 1:1 odds for the right testicle get multiplied by 2:1 to give 2:1 odds. The probability for his right ball to have cancer increases to 2/3. (The probability for his left testicle to have cancer is also 2/3, but they are not independent.)

In the Monty Hall problem, Monty Hall will reveal an empty door no matter what, so the hypothesis “the prize was behind your original door” explains the data just as well as “the prize was not behind your original door”. Therefore, the probability that the prize was behind your original door doesn’t change; there was no evidence for it. So your original door still has a 1/3 chance. So good Bayesians are not confused by the Monty Hall problem.