That depends on the quality of your randomness. If it's a person picking the number that definitely isn't a 1 in a million chance for most numbers. You need to get pretty specialized hardware before you can confidently say it's definitely an equal chance for every number every time.
You need a seed, and a previous hash. The seed is usually sourced from another random generator, that uses environmental noise and some (salted) hashing as well.
In practice, a precise clock and a few difficult to guess events like keyboard and mouse inputs are enough to get a descent seed.
Definitely not this. That wouldn't be random at all. For example, after picking 999,999 numbers you would be able to predict the next number with certainty. What it means is that every time you select a number, every number in the range has an equal probability of being selected.
Which means that there must be more state in the generator than is output as a random number; otherwise each time number X was produced then number Y would follow it. (Perhaps obvious, but I like stating the obvious.)
That's not a uniform distribution. Probability doesn't remember previous state, so the chance of picking the number 241 out of 1 million numbers remains exactly the same after picking 241 once. In particular, the chance of getting no duplicate numbers if you pick a million times, is very close to zero.
Technically, it's not actually uniform, or "random" - it only has to be indistinguishable from random, e.g. if you had access to a true random source and a pseudo-random one, it should be computationally infeasible to distinguish them.
Given a finite amount of sample.values, you can always find a polynomial to match the values.
You can only proclaim that something is "truly random" on epistemological grounds, assuming that true randomness is somehow exposed by the universe. This holds under quantum physics, but not under classical mechanics. Unless there is some quantum effect, RNGs based on fluid dynamics, like lava lamps, may be completely deterministic, we just don't know how to set their initial conditions precisely enough.to reproduce a phase trajectory.
Yes. Finding out the nature of a PRNG and its initial state would allow one to predict next values, at least theoretically, that is, if it can be computed faster than the values are generated by the process we're looking at.
But the presence or absence of such a function for a naturally occurring process is, again, an epistemological question. That is, whether we can reverse-engineer the universe deeply enough. Unless we find its source code, we're stuck with retrofitting some formulas, and a "random process" is such for which we can't retrofit any better description than a statistical one.
You're right that we can never find conclusive empirical evidence for something being truly random, but we can say with confidence that a PRNG is not random because we can look at the algorithm.
There's no evidence of true randomness though. There's only evidence of us having missing information to do the prediction of what any RNG would offer us next.
I am seeing this same/similar comment on this thread which has been posted by three quite new accounts. Is this quite normal on HN now or is it some bots farming engagement? Btw, I'm a new member here, knew about HN since a long time but never bothered to make an account up until now
Looks like one old-ish account with a number of submissions and comments, and two very-similarly-named copycats that are getting voted down to oblivion. Not very common here, but it happens.
>Is this quite normal on HN now or is it some bots farming engagement?
As you can see, my account is not new. I thought the comment will be funny. Not every comment should be terribly ingenious, long thought and crafted to show people how smart the commenter is. As a commenter you have the right to be spontaneous.
Why "without replacement"? The question doesn't seem to refer to multiple events. For just one event, it doesn't matter if you do with or without replacement, because there is nothing that can be replaced.
How did you get there? GP is talking about being dealt it, I think you've calculated the chance of it occurring in a round or something?
Wikipedia has GP's at 649739, so yeah 'almost' a million, roughly speaking. 4 / ((52 nCr 5) - (4 nCr 1)). (Four suits, one way to do it in each suit, deck of 52, five card hand.)
> "Scientists have calculated that the chances of something so patently absurd actually existing are millions to one.
But magicians have calculated that million-to-one chances crop up nine times out of ten."
-- Terry Pratchett
Making fun of the fact that if someone says "it's a million-to-one chance, but it might just work!" in fiction, you know it's going to work.
In _Guards! Guards!_ this is taken to the point that they reckon that it's not enough to hit the dragon with the arrow at the soft spot, they also have to try a whole bunch of improbable circumstances to get the chance to exactly 1 in a million. Because exactly 1 in a million is hard to achieve, as the article shows.
The cheese-loving aviators have the Swiss-cheese-model to visualize how small unimportant errors can stack up to a catastrophic outcome. Though in my opinion there's a flaw in this thinking as no sane cheese seller would shuffle their layers of cheese after cutting them: Hence 1-in-million chances happen all the time. https://www.aviationfile.com/swiss-cheese-model/
You'd think that but the Toledo fondue flood of 1973 says otherwise.
I can't find the wiki link, but Ronald O'Sullivan, who'd just taken over his father's cheese shop, was attempting to make a single block of swiss after a long week of selling the first 80% of several blocks to different restaurants. He took those cheese tails and stacked them up, not realizing the dangers he was putting everyone else in. It was later found that there were several other contributing factors; he hadn't used a properly certified cheese cutting blade (baloney!) and had reused the wax liner to store the off-cuts prior to reassembling the cheese that ultimately failed.
Some say you can still smell the swiss on hot august days.
Need a source on this, please. It sounds real at first look, but I cant find any links and it's very similar to the boston molasses flood of 1918 story, especially that last bit -
"The event entered local folklore and residents reported for decades afterwards that the area still smelled of molasses on hot summer days."
It's 100% made up. It's an attempt to make fun of people who would consider it unlikely that anyone would ever stack cheese as a way to mitigate safety risks.
Fondue can't cause a flood like the Great Molasses Flood because it's not capable of truly fully laminar flows.
Fondue is a colloidal suspension of cheese solids in a mixture of wine and melting agents and it shows complex rheological behavior. Unlike Newtonian fluids, where viscosity remains constant regardless of the applied shear rate, fondue demonstrates non-Newtonian features, especially shear-thinning. As the shear rate increases, its viscosity decreases, a phenomenon attributed to the alignment of colloidal particles in the direction of flow, interfering with the fluid and preventing it from filling every nook and cranny like a flood would.
Fondue's inability to achieve truly laminar flow is rooted in its viscoelastic properties. When subjected to stress, fondue exhibits both viscous and elastic characteristics, a behavior modeled by the Maxwell model in rheology. This model combines a viscous damper and an elastic spring to describe the material's response to applied stress. As fondue is subjected to shear stress, its structure becomes disrupted, leading to a breakdown in its ability to maintain a cohesive flow front. This disruption manifests as a turbulent flow, preventing the formation of a flood-like scenario similar to that caused by molasses.
I am a pilot, and in aviation related topics pilots tend to be almost ridiculously pedantic about stuff.
I want to congratulate you on writing the most pedantic thing I've ever read in my life. Truly a masterpiece. I can't wait to bust this out in my next aviation related discussion.
I don’t know if I’ve ever seen it investigated as a mathematical puzzle but there’s probably some interesting number stuff to be found in investigating the mathematics of an idealized swiss cheese risk model.
If you take a series of unit squares and from each one remove a random circle (ah, the origin of every protracted probability argument among mathematicians: a random circle selected from which distribution?), then stack them up… after n squares, what is the expected hole size? Or how many squares do you need to stack to reduce the hole size to below a particular threshold?
Circle intersection geometry is hard though. Probably easier to start with axis aligned square holes, which are what you get when you make your Swiss cheese out of milk from spherical cows.
I'm glad I'm not the only one bothered by the implied slice shuffling in this "model". Lol! We need to think of another real world setting where a stack of randomly hole-filled sheets would be naturally shuffled.
I reread Eric this weekend, somewhat on a whim. Real pleasure. I also had totally forgotten he came up with the premise of the Good Place. He had such so many ideas.
I read it as meaning that there's a much better chance that you're able to perform a trick, so the chance is the probability of filpping them for real, plus the chance of you being able to perform a trick to make it seem that you have.
You flip the coin, catch it, then present it on the back of your other hand.
With a bit of training you can feel the coin with your thumb when catching it to make sure you present the desired side. If you do it quickly enough people won't see you do it. The trick requires coins that have sides that feel distinctive.
Million-to-one chances are a running theme in a few of his books, but aiming specifically for it as in Guards, Guards! is one of the better awesome things in the Discworld
It seems like a pretty direct homage to the Infinite Improbability Drive from Hitchhiker's Guide to the Galaxy, which is created from thin air by working out exactly how improbable such a device is and plugging this number into a finite improbability generator (such machines themselves "often used to break the ice at parties by making all the molecules in the hostess's undergarments leap simultaneously one foot to the left, in accordance with the theory of indeterminacy.").
“Scientists have calculated that the chances of something so patently absurd actually existing are millions to one.
But magicians have calculated that million-to-one chances crop up nine times out of ten.”
― Terry Pratchett, Mort
Of course it's a very small step to make babies amounting to 3 times the current population of Switzerland (so assuming roughly a 50/50 female/male divide, everyone needs to make 6 babies on average, in the same year so they will still all be babies when the last one is born? that's probably impossible for the females but we are already way into the science-fiction category) and put them all in a committee.
The more I think about this, the funnier and more complicated this gets.
What do you mean, "that's *probably* impossible" to make 6 babies from the same mother in a single year? Takes about 9 months to output a human, you know?
Takes about 9 months to output N humans where N approaches to 1 on large averages as even twin births are like 1.2 percent.
Also many females cannot be pregnant again for a while after giving birth, even if we had sped up the pregnancy.
To average 6 babies, we need something like giving birth to triplets every 5 months in average, which improbable even if we had the technology, will and the economics that could support this.
But you know, "improbable" is such a funny word, especially out of context :)
This was a nice read. Takes me back to Probability 101, which was a fairly eye opening experience, not only because of you get to learn the basis of a lot of research (sampling, deviation, etc) but also because of the many counter intuitive behaviors of actual randomness. It was a little bit like learning the physics of math, if that makes sense.
I used to tell people I was an order of magnitude more likely to die from a traffic related accident driving to work than from any hostile action during the two years I was in Afghanistan. People seemed challenged by that.
You weren't joking, and actually it's much more than a magnitude.
The chances of dying in a traffic accident in US are between 0.9 and 1.2%, whereas the mortality rate of US military servicemen in Afghanistan has been below 0.004%.
Probably because they are different time frames. Your chances of dying in Afghanistan are over say a 4 year period. When someone says you have a 1% chance of dying in a car accident, that's spread over a lifetime. The chance of someone dying in a car accident in that 4 year period is much lower.
This often remains true even over smaller scales. For instance, between 2009 to 2012 I was in a combined arms battalion that deployed to Iraq twice and we experienced three deaths in combat and nine in traffic accidents near Fort Hood during those years. Another drowned in a lake near post. Also, one death due to suicide in theater.
I think getting killed by a meteorite is in that order of magnitude.
But it is a bit of a tricky question. Because you can get hit by a small space rock, it has already happened but it is extremely rare, much less than 1 in a million, and I don't know if there is a record of anyone dying from it. But there is also a small chance of a massive impact killing billions in our lifetime, and intermediate scale events like if the Tunguska event happened in a populated area.
There are records of people killed by meteorites, literally in single digits.
A simple statistical test would give you an idea. With 10B people on Earth, a "one in a million per lifetime chance" would happen to 10M people during their lifetimes. If we optimistically assume that a lifetime is 100 years, and the chances do not change with age, the event would affect 100k people every year.
Your calculation is wrong, there are a thousand millions to the billion. If there was a 1/1,000,000 chance across 10 billion people, it would be 10,000 people affected.
"Not in a million years"–something that might happen once in a million years for an individual–happens over 8000 times per year for people on earth. Many of them have smartphones with camera to record the event. That's why we have an actual video where quick brown fox jumps over the lazy dog.
My chance of dying because of a dinosaur-estinction-event-class asteroid hit is one in a 65 million years, thus every two and a half days on average a random person on earth dies being hit by an asteroid of that size.
This is quite a good point. People often fail to make the distinction between something that happens once every million years to all individual organisms separately versus something that happens once ever million years to the planet itself. Our language is usually ambiguous about this.
It may be nice to know the safety factors used for structural engineering of homes, offices and other regular buildings in the EU.
The Eurocode defines 3 consequence classes: CC1, CC2 and CC3.
CC1 has the lowest consequence and is used for regular homes, light industry and agriculture. The chance of dying as a result of structural failure is low, 0.001. The chance for a CC2 building (apartment buildings, offices, hotels etc.) is defined as moderate with 0.03. And CC3 is for special buildings, such as large stadiums, with a high risk of death on structural failure, 0.3. There are other factors that go in defining a consequence class however, including economic and social concerns.
The consequence class maps to the chance that we find it acceptable for a building to collapse in a given year. Causes can be anything, like extreme weather. For CC1 this is 1 in 100, for CC2 1 in 10.000, for CC3 a chance of 1 in 100.000.
So the chance one or more people die in a stadium during a heavy storm due to structural failure could be 1 in 300.000 in a year if you purely look at the statistics behind the structural safety standard.
The statistics map to simple reference values for the loads of wind, snow, rain, usage etc. and easy safety factors. For example CC2 has a safety factor of 1,5 over all variable loads.
If a CC2 building collapses, the expectation is that in only about 3% of the cases this leads to someone dying. I don't know the complete reasoning, but can imagine some factors of why this number is far below 100%: a building is not always in use, there are often warning signs with time to escape, and collapse can be localized (not the whole building).
Makes sense, so this is confounded by the number of people in the building.
30% for a CC3 seemed high to me initially (hence wondering if 0.3 really meant 0.3%). But since it actually means "in 30% of structural failures in CC3 buildings (e.g. a stadium), at least one person dies", it make much more sense because there are probably lots of people in the stadium.
I agree, it would likely have extremely diminishing returns in terms of lives saved to have more stringent safety requirements. Needs to be a balance after all.
«One time, someone asked me what my name was. I said, “Mark Xu.” Afterward, they probably believed my name was “Mark Xu.” I’m guessing they would have happily accepted a bet at 20:1 odds that my driver’s license would say “Mark Xu” on it.
The prior odds that someone’s name is “Mark Xu” are generously 1:1,000,000. Posterior odds of 20:1 implies that the odds ratio of me saying “Mark Xu” is 20,000,000:1, or roughly 24 bits of evidence. That’s a lot of evidence.»
«Extraordinary claims require extraordinary evidence, but extraordinary evidence might be more common than you think.»
Someone offering that bet though would also alter the odds though. Because why would someone offer such a ridiculous bet after you've told your name, unless they had special information about your name? Although it being a possible YouTube prank channel would make it also possible that you actually have a chance to win for the reactions.
This is part of a very important principle: if someone approaches you about something, you should be far more suspicious than if you had randomly picked someone off the street.
The most well known application of this is teaching kids to ask a random shopkeeper for help if they get lost, but to not get in a stranger's car when offered a lift.
> One of these days in your travels, a guy is going to show you a brand-new deck of cards on which the seal is not yet broken. Then this guy is going to offer to bet you that he can make the jack of spades jump out of this brand-new deck of cards and squirt cider in your ear. But, son, do not accept this bet, because as sure as you stand there, you're going to wind up with an ear full of cider.
This also applies to self defense. If someone has targeted you, the odds of you beating them in a fight is different than the odds you can take on a random person, or a random thief even
It's important to understand that when they said "bits" they didn't mean information in the Shannon entropy sense, but rather in the log-odds evidence sense.
Gaining a Shannon entropy bit means learning the answer to a yes-no question that had 1:1 odds.
Gaining a log-odds evidence bit means doubling your best-guess odds on a question you are uncertain about, from X:Y to (2X):Y.
One Shannon bit is worth arbitrarily many evidence bits, because a Shannon bit takes you from 1:1 odds to UNBOUNDEDLYHUGE:1 odds. So... yeah, actually, reading your username is worth infinite bits of log-odds evidence on what your username is! (Ignoring practical issues like the small chance of computer malfunctions, of course.)
And to answer your initial question: the 20 just came from the assertion they'd bet 20:1. That was arbitrary.
This isn't how the mathematics of odds works, as the GP correctly pointed out. An event which is 20:1 on is not 20 times more likely than certainty.
Going from 1:4 to 1:2 means that the event has become twice as likely. But going from 2:1 to 4:1 does not: it means that the complementary event has become half as likely.
Based on this, we can't do math with odds treating them identically to ratios.
If you do the math correctly, the two types of information measure are basically the same thing.
A log-odds of b bits means an odds of 2^b : 1 which means a probability of p = 2^b / (2^b + 1).
In the original comment, the evidence update was stated as going from 20:1 to 1:1000000 and it was claimed this was approximately 24 bits of evidence. The update is from 2^4.3:1 to 2^-19.9:1. Subtracting the exponents you get 4.3 - -19.9 = 24.2 which is approximately 24 as claimed. The "20" in 20:1 is correctly accounted for by the ~4 additional bits of evidence on top of updating from 1:1000000 to 1:1.
Clearly evidence bits behave very differently from entropy bits. Acquiring a single entropy bit is an update from 1:1 to 0:1 which is 2^0:1 to 2^-infinity:1. It's worth an unbounded number of evidence bits. It's important not to mix these two things up.
Yes, you are doing math with odds as though they are fractions or ratios, which is deeply incorrect. 20:1 is not the reciprocal of 1:20 but the complement. Odds ratios and similar calculations do not work like this. You can do this type of calculation using X:1 odds or 1:X odds but not both in the same calculation.
Or perhaps you can provide a reference to a justification of this type of calculation?
I think you're just wrong about needing everything to be in the form X:1 or 1:X. When I compute the ratio of 1000000:1 divided by 1:20 it gives 1000000:(1/20) then scaling both sides by the same factor gives 20000000:1.
Your reference definitely doesn't show a calculation of the type you are trying to do. Likelihood ratios are not the same as odds ratios; they do not have the problem I described.
I would be very surprised if you can find any reference at all to the number you describe as 'evidence bits', or anything equivalent, made by anyone who can show an understanding of basic probability, statistics, or information theory.
I understand how you get 20,000,000 as the answer to the calculation you carry out. My point is that that number is not meaningful in any way.
When you apply a statistical test, the various outcomes cause Bayesian updates that correspond to adding or subtracting fixed bits of evidence. When you repeat the test (and the repetitions are independent), the amount of bits of evidence you add or subtract remain the same. In other words, focusing on bits of evidence shows Bayesian updates behave like a biased random walk under repetition of a test and allow you to compute the properties of that walk.
For example, suppose you are trying to estimate how much rounding errors in a pseudo random number generator betray that it is not a true exact representation of the random process. One way to quantify this is to compute the expected bits of evidence revealed per call to the RNG.
This article seems to twist the definition of “extraordinary” to something clearly not intended by the original quote about claims and evidence.
Mark is a very common first name. Xu is a Chinese surname shared by over ten million people (according to Wikipedia). It’s entirely ordinary that someone would have this combination of names.
Don't ask me why, but I had to urge to look up the number of Chinese surnames: About 2000 are currently in use, half the Han Chinese population uses just 19 surnames.
Quite low compared to the 850 THOUSAND (another source has more than a million) surnames used in Germany, twice as much as in Spain bespite it have a tradition of double-names. I guess Germany being such a central state with many migration waves over the last 600~800 years since family names have become common.
That’s a good analogy for my point, because just having a lottery ticket isn’t extraordinary even though all of the tickets are unique.
If somebody claims they are holding a winning lottery ticket and will sell it to me for a 50% discount because they are leaving the country in two hours and don’t have time to cash it out, that’s an extraordinary claim and I would need extraordinary evidence to take the deal.
If someone says their name is Mark Wu, it’s like saying they are holding a non-winning lottery ticket with serial number 12345654321. It’s at best a curiosity.
24 bits seems about right for the information content of six Latin characters arranged in a pronounceable English orthography (the ‘X’ has pretty high information value though).
There's something so wrong with that logic. Imagine it as:
Mark: "I've thought of a number between one and a billion and written it down. The number I wrote down was 6,822,172"
Recursing (you): "ok"
Mark: "ohh you believe me? Then let's take a bet, if I did write down 6,822,172 then you win. If I wrote down any other number between one and a billion then I win".
Would you take that bet? I think you'd be suspicious.
Mark: "I predict that Recursing would take that bet, happily, because why wouldn't they believe me? Therefore them taking the bet is very strong evidence that I actually did write down 6,822,172".
You can't use your prediction about someone else's behaviour as evidence!
An interesting read. I was secretly hoping it would have delved more into the psychology of how we perceive that chance, what kind of biases we have. I feel we have a tendency of saying "it never fails" on probabilities which turn out to be much bigger than one in a million.
Your grandmother very probably doesn't work in a rail gang.
ELEVEN members of a railway maintenance crew had to be taken to hospital on Saturday after lightning struck train tracks they were working on in WA’s Goldfields-Esperance region.
198 comments
[ 2.1 ms ] story [ 244 ms ] threadIn practice, a precise clock and a few difficult to guess events like keyboard and mouse inputs are enough to get a descent seed.
You can only proclaim that something is "truly random" on epistemological grounds, assuming that true randomness is somehow exposed by the universe. This holds under quantum physics, but not under classical mechanics. Unless there is some quantum effect, RNGs based on fluid dynamics, like lava lamps, may be completely deterministic, we just don't know how to set their initial conditions precisely enough.to reproduce a phase trajectory.
But the presence or absence of such a function for a naturally occurring process is, again, an epistemological question. That is, whether we can reverse-engineer the universe deeply enough. Unless we find its source code, we're stuck with retrofitting some formulas, and a "random process" is such for which we can't retrofit any better description than a statistical one.
Only if the person isn't aware of the issues involved.
https://images.app.goo.gl/aqh2AP5tj7JBkVKZ9
As you can see, my account is not new. I thought the comment will be funny. Not every comment should be terribly ingenious, long thought and crafted to show people how smart the commenter is. As a commenter you have the right to be spontaneous.
Yeah I did find it funny, no doubt. Was just asking of general culture here
Therefore if a human picks a number between 1 and 1 million, there's only a 1 in a million chance that the number was picked randomly.
Being Born on February 29th. NO
Wikipedia has GP's at 649739, so yeah 'almost' a million, roughly speaking. 4 / ((52 nCr 5) - (4 nCr 1)). (Four suits, one way to do it in each suit, deck of 52, five card hand.)
I can't think of any common form of poker where it would be ~6000:1
Winning ~3-4 times at the roulette when betting on numbers?
-- Terry Pratchett
Making fun of the fact that if someone says "it's a million-to-one chance, but it might just work!" in fiction, you know it's going to work.
In _Guards! Guards!_ this is taken to the point that they reckon that it's not enough to hit the dragon with the arrow at the soft spot, they also have to try a whole bunch of improbable circumstances to get the chance to exactly 1 in a million. Because exactly 1 in a million is hard to achieve, as the article shows.
I can't find the wiki link, but Ronald O'Sullivan, who'd just taken over his father's cheese shop, was attempting to make a single block of swiss after a long week of selling the first 80% of several blocks to different restaurants. He took those cheese tails and stacked them up, not realizing the dangers he was putting everyone else in. It was later found that there were several other contributing factors; he hadn't used a properly certified cheese cutting blade (baloney!) and had reused the wax liner to store the off-cuts prior to reassembling the cheese that ultimately failed.
Some say you can still smell the swiss on hot august days.
"The event entered local folklore and residents reported for decades afterwards that the area still smelled of molasses on hot summer days."
https://en.m.wikipedia.org/wiki/Great_Molasses_Flood
Fondue is a colloidal suspension of cheese solids in a mixture of wine and melting agents and it shows complex rheological behavior. Unlike Newtonian fluids, where viscosity remains constant regardless of the applied shear rate, fondue demonstrates non-Newtonian features, especially shear-thinning. As the shear rate increases, its viscosity decreases, a phenomenon attributed to the alignment of colloidal particles in the direction of flow, interfering with the fluid and preventing it from filling every nook and cranny like a flood would.
Fondue's inability to achieve truly laminar flow is rooted in its viscoelastic properties. When subjected to stress, fondue exhibits both viscous and elastic characteristics, a behavior modeled by the Maxwell model in rheology. This model combines a viscous damper and an elastic spring to describe the material's response to applied stress. As fondue is subjected to shear stress, its structure becomes disrupted, leading to a breakdown in its ability to maintain a cohesive flow front. This disruption manifests as a turbulent flow, preventing the formation of a flood-like scenario similar to that caused by molasses.
I want to congratulate you on writing the most pedantic thing I've ever read in my life. Truly a masterpiece. I can't wait to bust this out in my next aviation related discussion.
If you take a series of unit squares and from each one remove a random circle (ah, the origin of every protracted probability argument among mathematicians: a random circle selected from which distribution?), then stack them up… after n squares, what is the expected hole size? Or how many squares do you need to stack to reduce the hole size to below a particular threshold?
Circle intersection geometry is hard though. Probably easier to start with axis aligned square holes, which are what you get when you make your Swiss cheese out of milk from spherical cows.
https://www.npr.org/2004/02/24/1697475/the-not-so-random-coi...
With a bit of training you can feel the coin with your thumb when catching it to make sure you present the desired side. If you do it quickly enough people won't see you do it. The trick requires coins that have sides that feel distinctive.
This sentence desperately needs parentheses. Took me five minutes to parse correctly.
> If we guess a President will serve on average about 6 years, then [1 in (6 times 4.0 million =) 24 million] babies will someday be President.
Switzerland does have a committee of 7 people as president, only a small step from that to 24 million babies.
The more I think about this, the funnier and more complicated this gets.
It can also be done months faster getting multiple humans though.
It’s probably impossible for a population to average that rate though.
Takes about 9 months to output N humans where N approaches to 1 on large averages as even twin births are like 1.2 percent.
Also many females cannot be pregnant again for a while after giving birth, even if we had sped up the pregnancy.
To average 6 babies, we need something like giving birth to triplets every 5 months in average, which improbable even if we had the technology, will and the economics that could support this.
But you know, "improbable" is such a funny word, especially out of context :)
> It's true, if you're on you deathbed and you've only eaten one spider seven more come along and jump right in there at that last second.
I figured it was that sort of reasoning that got us to 24 million babies.
https://archive.ph/fNeaH
HN comments: https://news.ycombinator.com/item?id=5145268
The chances of dying in a traffic accident in US are between 0.9 and 1.2%, whereas the mortality rate of US military servicemen in Afghanistan has been below 0.004%.
That's 3 order of magnitudes.
But it is a bit of a tricky question. Because you can get hit by a small space rock, it has already happened but it is extremely rare, much less than 1 in a million, and I don't know if there is a record of anyone dying from it. But there is also a small chance of a massive impact killing billions in our lifetime, and intermediate scale events like if the Tunguska event happened in a populated area.
A simple statistical test would give you an idea. With 10B people on Earth, a "one in a million per lifetime chance" would happen to 10M people during their lifetimes. If we optimistically assume that a lifetime is 100 years, and the chances do not change with age, the event would affect 100k people every year.
Then the "one in a million per lifetime" chance would be an event that happens to about a hundred people every year, on average.
Winning a big lottery is within the right ballpark. Flying to space is definitely more rare.
> Q > What has a 1 in a million chance
> A > https://news.ycombinator.com/item?id=38907620
The Eurocode defines 3 consequence classes: CC1, CC2 and CC3. CC1 has the lowest consequence and is used for regular homes, light industry and agriculture. The chance of dying as a result of structural failure is low, 0.001. The chance for a CC2 building (apartment buildings, offices, hotels etc.) is defined as moderate with 0.03. And CC3 is for special buildings, such as large stadiums, with a high risk of death on structural failure, 0.3. There are other factors that go in defining a consequence class however, including economic and social concerns.
The consequence class maps to the chance that we find it acceptable for a building to collapse in a given year. Causes can be anything, like extreme weather. For CC1 this is 1 in 100, for CC2 1 in 10.000, for CC3 a chance of 1 in 100.000.
So the chance one or more people die in a stadium during a heavy storm due to structural failure could be 1 in 300.000 in a year if you purely look at the statistics behind the structural safety standard.
The statistics map to simple reference values for the loads of wind, snow, rain, usage etc. and easy safety factors. For example CC2 has a safety factor of 1,5 over all variable loads.
Does this mean 3% or 0.03%?
30% for a CC3 seemed high to me initially (hence wondering if 0.3 really meant 0.3%). But since it actually means "in 30% of structural failures in CC3 buildings (e.g. a stadium), at least one person dies", it make much more sense because there are probably lots of people in the stadium.
«One time, someone asked me what my name was. I said, “Mark Xu.” Afterward, they probably believed my name was “Mark Xu.” I’m guessing they would have happily accepted a bet at 20:1 odds that my driver’s license would say “Mark Xu” on it.
The prior odds that someone’s name is “Mark Xu” are generously 1:1,000,000. Posterior odds of 20:1 implies that the odds ratio of me saying “Mark Xu” is 20,000,000:1, or roughly 24 bits of evidence. That’s a lot of evidence.»
«Extraordinary claims require extraordinary evidence, but extraordinary evidence might be more common than you think.»
The most well known application of this is teaching kids to ask a random shopkeeper for help if they get lost, but to not get in a stranger's car when offered a lift.
(Guys and Dolls)
If an attractive lady walks up to me at a party, first thing I'm doing is asking for references.
The question is are you using the name on your drivers license, which is probably the one on your birth certificate.
> 20 coin tosses (by me) all coming up Tails. YES
> If you tossed the coins then the first answer would be NO, unless I'm very confident you lack the ability to fool me …
Using the same argument I would accept infinite odds that my username is quickthrower2 so there is infinite information?
Gaining a Shannon entropy bit means learning the answer to a yes-no question that had 1:1 odds.
Gaining a log-odds evidence bit means doubling your best-guess odds on a question you are uncertain about, from X:Y to (2X):Y.
One Shannon bit is worth arbitrarily many evidence bits, because a Shannon bit takes you from 1:1 odds to UNBOUNDEDLYHUGE:1 odds. So... yeah, actually, reading your username is worth infinite bits of log-odds evidence on what your username is! (Ignoring practical issues like the small chance of computer malfunctions, of course.)
And to answer your initial question: the 20 just came from the assertion they'd bet 20:1. That was arbitrary.
Going from 1:4 to 1:2 means that the event has become twice as likely. But going from 2:1 to 4:1 does not: it means that the complementary event has become half as likely.
Based on this, we can't do math with odds treating them identically to ratios.
If you do the math correctly, the two types of information measure are basically the same thing.
In the original comment, the evidence update was stated as going from 20:1 to 1:1000000 and it was claimed this was approximately 24 bits of evidence. The update is from 2^4.3:1 to 2^-19.9:1. Subtracting the exponents you get 4.3 - -19.9 = 24.2 which is approximately 24 as claimed. The "20" in 20:1 is correctly accounted for by the ~4 additional bits of evidence on top of updating from 1:1000000 to 1:1.
Clearly evidence bits behave very differently from entropy bits. Acquiring a single entropy bit is an update from 1:1 to 0:1 which is 2^0:1 to 2^-infinity:1. It's worth an unbounded number of evidence bits. It's important not to mix these two things up.
Or perhaps you can provide a reference to a justification of this type of calculation?
I think you're just wrong about needing everything to be in the form X:1 or 1:X. When I compute the ratio of 1000000:1 divided by 1:20 it gives 1000000:(1/20) then scaling both sides by the same factor gives 20000000:1.
I would be very surprised if you can find any reference at all to the number you describe as 'evidence bits', or anything equivalent, made by anyone who can show an understanding of basic probability, statistics, or information theory.
I understand how you get 20,000,000 as the answer to the calculation you carry out. My point is that that number is not meaningful in any way.
For example, suppose you are trying to estimate how much rounding errors in a pseudo random number generator betray that it is not a true exact representation of the random process. One way to quantify this is to compute the expected bits of evidence revealed per call to the RNG.
Mark is a very common first name. Xu is a Chinese surname shared by over ten million people (according to Wikipedia). It’s entirely ordinary that someone would have this combination of names.
Analogy: someone has a winning lottery ticket. Is it the one in your hand? Probably not.
If somebody claims they are holding a winning lottery ticket and will sell it to me for a 50% discount because they are leaving the country in two hours and don’t have time to cash it out, that’s an extraordinary claim and I would need extraordinary evidence to take the deal.
If someone says their name is Mark Wu, it’s like saying they are holding a non-winning lottery ticket with serial number 12345654321. It’s at best a curiosity.
Mark: "I've thought of a number between one and a billion and written it down. The number I wrote down was 6,822,172"
Recursing (you): "ok"
Mark: "ohh you believe me? Then let's take a bet, if I did write down 6,822,172 then you win. If I wrote down any other number between one and a billion then I win".
Would you take that bet? I think you'd be suspicious.
Mark: "I predict that Recursing would take that bet, happily, because why wouldn't they believe me? Therefore them taking the bet is very strong evidence that I actually did write down 6,822,172".
You can't use your prediction about someone else's behaviour as evidence!
A wave hit it?
A wave hit the ship.
Is that unusual?
Oh yeah, at sea? Chance in a million!
https://www.youtube.com/watch?v=3m5qxZm_JqM
reminds me of https://xkcd.com/795/