Replace X with any practitioners subject to sufficient risk as a result of their practice.
I first heard it in the context of mushroom foraging.
This is called Stage 1 in the Gordon Model of learning: unconscious incompetence.
Rather, there is a certain amount of objective risk in alpine environments, and the more time you put yourself in that environment, especially in locations you aren't familiar with, the greater the chance that something will eventually go wrong.
I'm always surprised by the number of famous alpinists who weren't killed on their progressive, headline-capturing attempts but rather on training attempts and lesser objectives.
You hear a lot about people who get seriously injured riding who are often professionals or people who ride competitively at a high level. They are doing dangerous things and doing a lot of them.
We don't think it is that dangerous for people who ride at the level we do, out of maybe 15 years we've had one broken bone.
The other day I noticed that we had acquired a used horse blanket from another barn in the area which is a running joke at our barn because of their bad safety culture. They are a "better" barn than ours in that they are attached to the show circuit at a higher level than the bottom, but we are always hearing about crazy accidents that happen there. When I was learning to ride there they had a confusing situation almost like
https://aviation-safety.net/database/record.php?id=19810217-...
with too many lessons going on at once where I wound up going over a jump by accident after a "near miss" in which I almost did. (I never thought I could go over a jump and survive, as it was I had about two seconds to figure out that I had to trust the horse and hang on and I did alright...)
Pretty good if you go climbing 10 times a year. Pretty bad if you go 1000 times.
They wouldn't be famous if they didn't succeed on headline-capturing attempts and there are only so many you can realistically do in life. They are dead however as doing dangerous things often enough will kill a substantial number of practitioners.
Most of you have probably heard of it in the context of fighter pilots doing riskier and riskier maneuvers, but it seems to apply to drivers who speed a lot. 80 starts seeming really slow to them after doing it for years.
* https://flightsafety.org/asw-article/normalization-of-devian....
https://www.youtube.com/watch?v=Ljzj9Msli5o
https://www.youtube.com/watch?v=jWxk5t4hFAg
and the uploader references some further links:
https://www.fireengineering.com/leadership/firefighter-safet...
https://www.flightsafetyaustralia.com/2017/05/safety-in-mind...
and references this book (about the Challenger Disaster):
https://www.amazon.com/gp/product/B011DAS53Y/
which has an overview here:
http://web.mit.edu/esd.83/www/notebook/The%20Challenger%20La...
including these two excerpts I found interesting in this context: "Chapter nine she explains how conformity to the rules, and the work culture, led to the disaster, and not the violation of any rules, as thought by many of the investigators. She concludes her book with a chapter on lessons learned."
"She mainly emphasizes on the long-term impact of institutionalization of the political pressure and economic factors, that results in a “culture of production”."
Every other manned space vehicle had an escape system. The crew of the Challenger was not killed by the failure of the SRB or the explosion of the external tank, but rather when the part of the orbiter they were in hit the ocean. They could have build this into a reinforced pod with parachutes or some other ability to land but they chose not to because they wanted to have the payload section in the rear.
In the case of Columbia it was the fragile thermal protection system that did the astronauts in. There was a lot of fear in the first few flights that the thermal tiles would get damaged and failed and once they thought they'd dodged that bullet they didn't worry about it so much.
"Normalization of deviance" was a formal process in the case of the space shuttle of there being meetings where people went through a list of a few hundred unacceptable situations that they convinced themselves they could accept, often by taking some mitigations.
When the design was finalized it was estimated that a loss of vehicle and crew would happen about 2%-3% of the the time which was about what we experienced. (Originally they planned to launch 50 missions a year which would have meant the continuous trauma of losing astronauts and replacing vehicles.)
It's easy to come to the conclusion that it was a particular scandal that one particular concern got dismissed during a "normalization of deviance" meeting but given a poorly designed vehicle it was inevitable that after making good calls for thousands of concerns there would be a critical bad call.
"Normalization of deviance" is frequently used for a phenomenon entirely different than what Vaughn is talking about, something informal that happens at the level of individuals and small groups. That is, the forklift operators who come to the conclusion it is OK to smoke pot at work, the surgeon who thinks it is OK to not wash his hands, etc. A group can pressure people to do the right things here, but it's something different from the slow motion horror of bureaucracy that tries to do the right thing but cannot.
The standard protocol was to use shims between the halves, as allowing them to close completely could result in the instantaneous formation of a critical mass and a lethal power excursion. Under Slotin's own unapproved protocol, the shims were not used and the only thing preventing the closure was the blade of a standard flat-tipped screwdriver manipulated in Slotin's other hand. Slotin, who was given to bravado, became the local expert, performing the test on almost a dozen occasions, often in his trademark blue jeans and cowboy boots, in front of a roomful of observers. Enrico Fermi reportedly told Slotin and others they would be "dead within a year" if they continued performing the test in that manner. Scientists referred to this flirting with the possibility of a nuclear chain reaction as "tickling the dragon's tail", based on a remark by physicist Richard Feynman, who compared the experiments to "tickling the tail of a sleeping dragon".
On the day of the accident, Slotin's screwdriver slipped outward a fraction of an inch while he was lowering the top reflector, allowing the reflector to fall into place around the core. Instantly, there was a flash of blue light and a wave of heat across Slotin's skin; the core had become supercritical, releasing an intense burst of neutron radiation estimated to have lasted about a half second. Slotin quickly twisted his wrist, flipping the top shell to the floor. The heating of the core and shells stopped the criticality within seconds of its initiation, while Slotin's reaction prevented a recurrence and ended the accident. The position of Slotin's body over the apparatus also shielded the others from much of the neutron radiation, but he received a lethal dose of 1,000 rad (10 Gy) neutron and 114 rad (1.14 Gy) gamma radiation in under a second and died nine days later from acute radiation poisoning.
I'm guessing that falling from a cliff is "better" than dying from a poisonous mushroom. The latter scares the hell out of me. The former is a glorious ride until the ride is over (regrettably).
If you sense you're falling to death, it wont be too glorious (personally), but freakish. It can also always fail to bring death!
Kind of. However, you already know that the first N outings didn't have a disaster. So those should be discarded from your analysis.
Doing it N times more has a lot of risk, doing it the N+1th time has barely any.
In this case [0], a skydiver forgot to put on his parachute...
https://reverentialramblings.com/2018/08/15/the-skydiver-who...
Many times if I wear a tight jacket in the car, I forget to put my seat belt on, because I unconsciously mistake the pressure of the jacket for the seatbelt's, even though putting on a seat belt is usually the first thing I do.
Poor guy.
Also, when I read
> I’m hoping you can you forgive me as a minister of religion for likening this story to a spiritual cautionary tale. Yes, we do need to live each day as if it might be our last.
I thought, "Hmm, sounds adventist", and sure enough :-)
In other words , the difference between the turkey and the climber is the climber knows the odds (at least nominally) , and it’s important .
The next three months are no riskier than your first three months were. They don't become more risky because they will add up to 15 months total -- once you've already finished the first 12 without incident.
Or a few minutes ... or 20 years.
That's the thing w/ statistically independent trials.
You could win 100mm in the lottery (true statement!)
Lottery tickets are a good investment (almost always, false statement).
Planning on "well it could happen, technically" isn't a good approach.
Your chance of winning goes from No Chance to A Chance, which is an infinite improvement.
It's true that you can never win a lottery you don't enter, but the expected value of that ticket is vastly lower than what you paid for it. That means, as an investment, your $10 will be expected to do better in literally anything with a positive return.
If you are buying > $10 worth of dreaming (for you), fine - but that's consumption.
Also, “not really in a lot of danger”? Those odds are worse than that of a 100 year old in the USA (they have a life expectancy of over two years)
Certainly, as an additional risk, it’s high.
Though I'm not sure where they got their figure from, because there isn't an “expected time to live”; there's a 90% probability to live time, a 5% probability to live time…
(215/216)^450 ≈ 0.124
(215/216)^645 ≈ 0.0501
(215/216)^149 ≈ 0.501Basically, the problem is that you can't just multiply it all together.
(1/6) ^ 3 is correct, and the probability of rolling 3 sixes is indeed 1/216 today, but if you repeat independent events, you don't just add up the probability.
Imagine instead of dice it's coins, and it's only two. Your odds of getting HH today are 1/4, but the odds of getting HH by day four are not now 4/4. We know that it's possible, although unlikely, you could flip coins for the rest of your life and NEVER get two heads. So we know that you can't ever have odds of 4/4 (or 1), only odds that approach 1. So that means that we can't say 216 days from now will be 216/216.
Instead, you need to work out the probability of the event NOT happening, and then repeatedly NOT happening independently (so we can multiply together to get the probability.
For our four coins, the probability of NOT getting HH is 3/4. On Day 2, the probability of NOT getting HH on both occasions will be (3/4)×(3/4), (9/16, 56.25%). By day 3, it will be (3/4) × (3/4) × (3/4), or 27/64. On day 4, it'll be 81/256, or 31.6%. Now we can subtract from 1, to work out that by day 4, the odds of us having hit HH are almost 70%.
As RandomSwede explains, there's a 50% chance that you will have rolled three sixes by day 149. By day 496, you're down to 10%.
runs <- 10000
x <- vector(mode = "numeric", length = runs)
for (i in 1:runs){
while (sum(sample(1:6, size = 3, replace = TRUE)) != 18){
x[i] <- x[i] + 1
}
}
summary(x)
quantile(x, c(0.5, 0.8, 0.9))
> summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 62.0 149.0 216.2 300.0 1902.0
> quantile(x, c(0.5, 0.8, 0.9))
50% 80% 90%
149 350 495
The numbers very much agree with you. The median is 149. The 90th is 495 in the simulation, which is close enough to 496. There is very much a long tail in the data. So, the median and the average will not be the same. Is it a coincidence that mean is a 216?
Thinking about it doesn't make me feel like I'm solving a maths problem. I start stacking ideas and concepts in a way which makes me feel like I'm overlaying them in a way which is incorrect.
It makes me feel like I'm solving a riddle, which hints to me that maybe it's actually a question of semantics and definitions rather than a maths problem.
At sufficient scale, even incredibly unlikely things become quite probable.
runs <- 10000
x <- vector(mode = "numeric", length = runs)
for (i in 1:runs){
while (sum(sample(1:6, size = 3, replace = TRUE)) != 18){
x[i] <- x[i] + 1
}
}
summary(x)
quantile(x, c(0.5, 0.8, 0.9))
> summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 62.0 149.0 216.2 300.0 1902.0
> quantile(x, c(0.5, 0.8, 0.9))
50% 80% 90%
149 350 495
The numbers very much agree with you. The median is 149. The 90th is 495 in the simulation, which is close enough to 496. There is very much a long tail in the data. So, the median and the average will not be the same. Is it a coincidence that mean is a 216?
Iteration counts gathered with Python and a (manual) binary search (actually faster than writing code).
runs <- 100000
x <- vector(mode = "numeric", length = runs)
for (i in 1:runs){
while (sum(sample(1:8, size = 3, replace = TRUE)) != 24){
x[i] <- x[i] + 1
}
}
summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0 146.0 353.0 511.8 708.0 5112.0
quantile(x, c(0.5, 0.8, 0.9))
50% 80% 90%
353 824 1187
After a long life of rock climbing, there's no significant risk of doing it one last time or 10 last times (ignoring the effect of old age itself and whatever).
But when you're in earlier stages of your life, you're asking a different question: You're asking, is this something I want to do hundreds or thousands of times in my life, knowing that each of those times has a small chance of ending my life? This becomes a completely different question.
If I'm 35, maybe I will climb 30 times per year on average for 30 years until I'm 65. That's 900 climbs in total. If my goal is to not die or experience serious injury from rock climbing even once in my life, I have to consider the chance that any one of those 900 climbs will result in serious injury or death. I don't know the numbers for the risks involved, but it seems reasonable to be cautious.
Maybe I don't want to give up on rock climbing altogether, but maybe I can scale it back. If I limit myself to 1 climb per year, that's 30 climbs in total. Much lower risk than with 900 climbs.
This is not a logical fallacy.
Now, making that decision at the outset does make sense, because it will drastically reduce the number of climbs you make in your life compared to climbing frequently throughout your life, and rock climbing while young is less risky than rock climbing while old.
But importantly, I don't think that's what GP did. It sounds to me like GP spent their youth climbing a lot without considering their mortality, but then decided to scale back because they realized climbing that often for the rest of their life would be dangerous. Maybe they spent the time from 20 to 35 climbing 30 times per year, in keeping with my earlier example. That means they've already climbed 450 times. Risky, but they made it through alive. At 35, they start to consider their own mortality, and they have the choice between climbing 900 more times by keeping to their current rate, and climbing 30 more times by reducing their rate (or something in between). Deciding to scale back makes sense.
There is no logical fallacy.
This makes a lot of sense, as when you're younger frequent climbing would help you to develop proficiency quickly and your body allows you to joy it fully. Plus the social benefits are probably higher when younger.
Once you're older, it's potentially less enjoyable (as your body ages) and you don't need to worry as much about rapidly gaining proficiency.
None of this intended to cast aspersions on rock climbing in particular, just pointing out that a reasonable person, understanding independence of events and not falling prey to any fallacy, could reasonably make this decision based on their personal risk tolerance
The more frequently you take a risk, the greater the chance that risk materialises.
Parent wants to lower their overall risk, but doesn't want to stop climbing entirely. So they climb less often.
If an individual decides their risk tolerance is that they will not accept a one in a million chance of injury from rock climbing, how is their analysis incorrect?
If your tolerance is X% death/life, you can calculate the climbing frequency that falls below the threshold.
On the plus side, if you assume the events are independent, you can recalculate and increase the frequency after each climb.
So it's not about how often they've done it over their lifetime so far, but about how many times they will be doing it over the rest of their life.
It's a convincing fallacy because sometimes you do take N+1 steps. But just like in the article, heuristics aren't always right.
The argument can certainly be used in a fallacious manner (e.g. by greatly exaggerating the probability of the further steps, saying they are inevitable if the first step is taken, etc.). It's logically valid to say that the first step enables subsequent steps to be taken.
Edit: I'd say that the slippery slope is perfectly valid rule of thumb in a lot of 'adversarial' situations. Once one side makes an error or fails somehow, the balance between the two sides can be disrupted leading to one 'side' gaining momentum. Just as between people, a similar 'adversarial' process can occur within the minds of individuals: between two ideas or patterns of thought/behaviour, one idea can gain momentum after a decision has been reached. Precedence is a strong force.
Reminds me of Terry Pratchett quote "No excuses. No excuses at all. Once you had a good excuse, you opened the door to bad excuses.”
Full quote is fifth here: <https://www.goodreads.com/work/quotes/819104-thud>
Just because you can justify the next climb on the same basis, that doesn't mean you will. You could decide that you've already tested the odds one too many times.
Do you really think this slippery slope argument is a fallacy? FWIW, wikipedia acknowledges slippery slope can be a legit argument when the slope, and it's chain of consequences, are actually real. https://en.m.wikipedia.org/wiki/Slippery_slope . Indeed, this is the very basis of mathematical induction.
> The fallacious sense of "slippery slope" is often used synonymously with continuum fallacy, in that it ignores the possibility of middle ground and assumes a discrete transition from category A to category B. In this sense, it constitutes an informal fallacy.
"If you take N steps, you will take N+1 steps" is a fallacy whenever it's possible that you won't take N+1 steps.
"Not A -> Not B" is different logic than "A -> B". A is necessary but not sufficient for B.
Of course if you add in "you could decide that you've already tested the odds one too many times" then it's a fallacy to invoke slippery slope because an off-ramp is explicitly specified. In this case slippery slope was mentioned only because N was dismissed as irrelevant.
A fallacy should be a incorrect shape of an argument, a incorrect reasoning, not just a false statement.
Otherwise, it's just a regular d argument.
If you've made it Jan 1 to July 1 months without an accident, the chances of you making it to Dec 31 are now better than they were on Jan 1 -- because now they are just the chances of you making it six months, not a year.
The chances of flipping 6 heads in a row are 1/64. But if I've already flipped 3 in a row... the chances of flipping three _more_ heads in a row is 1/8, the same as always for flipping 3 heads in a row. The ones that already happened don't effect your future chances.
You might still die in one of the next 20 instances. But you've added a lot more not-dead time in between them!
Saying "I can do one more with minimal added risk" every single time after not dying is true and yet pointless, because it's not a given that "minimal added risk" = "not dying." It's survivorship bias to not think frequency doesn't affect the cumulative odds of your future planning solely because you've already done a lot of trials.
Indoor climbing, and especially bouldering, can be a lot of fun at the right gym, and with dramatically reduced risk of death (though injury is still a very real possibility, I say, recalling all the time I spent nursing my sprained ankle).
The 1000th time you go climbing the chances of dying are still 1/1000.
If you get 100 heads in a row, the 101th time you launch a coin the chance of getting heads is still 50%.
"What are my chances of dying in a climbing accident", and
"What are my chances of dying today if I go climbing".
If you are on a plane, you* have a lower risk of some kinds of cancer than the airline staff do. This has nothing to do with the flight you are both on, and everything to do with accumulated flights
"you*" = for most people, i.e. barring a counteracting risk factor.
For rock climbing, you're probably right. I remember training in a climbing hall, when I saw someone falling off the highest wall. The tenant of the hall didn't look surprised at all. Apparently, it happens frequently.
That being said, if you serious about security, I'm sure the risk can be minimal.
See e.g. https://blogs.bmj.com/bjsm/2018/12/12/pedal-power-the-health...
It's essential if you want to:
* make money by counting cards at Blackjack (the odds are a function of how many 10 cards are left in the deck)
* make money at the racetrack with a system like this https://www.amazon.com/Dr-Beat-Racetrack-William-Ziemba/dp/0...
* turn a predictive model for financial prices into a profitable trading system
In the case where the bet loses money you can interpret Kelly as either "the only way to win is not to play" or "bet it all on Red exactly once and walk away " depending on how you take the limit.
The general idea is about choosing an action that maximises the expected logarithm of the result.
In practise this means, among other things, not choosing an action that gets you close to "ruin", however you choose to measure the result. Another way to phrase it is that the Kelly criterion leads to actions that avoid large losses.
https://en.wikipedia.org/wiki/Kelly_criterion
"The Kelly bet size is found by maximizing the expected value of the logarithm of wealth, which is equivalent to maximizing the expected geometric growth rate"
In real life people often choose to make bets smaller than the Kelley bet. Part of that is that even if you have a good model there are still "unknown unknowns" that will make your model wrong some of the time. Also most people aren't comfortable with the sharp ups and downs and probability of ruin you have with Kelley.
1) The Kelly criterion is a general decision rule not limited to bet sizing. Bet sizing is just a special case where you're choosing between actions that correspond to different bet sizes. The Kelly criterion works very well also for other actions, like whether to pursue project A or B, whether to get insurance or not, and indeed whether to sleep under a tree or on a rock.
2) The Kelly criterion is not limited to what people would ordinarily think of as "wealth". It applies just as well to anything you can measure with some sort of utility where compounding makes sense.
The best overview I've found so far is The Kelly Capital Growth Investment Criterion[1], which unfortunately is a thick collection of peer-reviewed science, so it's very detailed and heavy on the maths, too.
[1]: https://www.amazon.com/KELLY-CAPITAL-GROWTH-INVESTMENT-CRITE...
There's actually a similar though experiment that might seem even more bizarre: I could tell you "give me $100 or I will kill you tomorrow" and you probably wouldn't give me the $100. That's because when it comes down to it, humans don't see the loss of their life as that big a deal as one might think. It's a big deal, of course, but in combination with the low likelihood, still not big enough to forgo the $100.
One-time games and repeated games have different strategies.
Life is a repeated game of decisions that compound on each other, so that difference is irrelevant.
If the chance was actually 1:5000, the longevity of trees would be similar to that of hunters sleeping under them - or lower, since hunters can sometimes avoid the hazard, but trees are exposed to it every night (and all day as well). Actual data on tree mortality seems to indicate that the chances are much lower than this. Almost certainly they do not make sleeping under trees a significant risky activity.
There’s a real dearth of info around coconut fatalities. This 1984 paper examined a 4 year timespan of all trauma-related admissions to one hospital in Papua New Guinea. 9 of them (2.5% overall) stemmed from coconuts. In 3 of the cases, all children, the patients slipped into coma.
From what I’ve read elsewhere (no good links, sorry), coconut injuries worldwide seem to have fallen significantly due to better harvesting practice - the age of a coconut and their chance of falling appear to be linked. While I can’t find a good paper saying this, I do buy it.
So, perhaps not 1:5000, but (at least at one point in time) definitely a risk.
Is it possible that both the original teller, the reteller and you too, are starting from the (unfounded) assumption that the hunters know what they're talking about, and are adapting the facts to fit this assumption?
There could be another reason why sleeping in the forest was dangerous – e.g., poisonous snakes or dangerous animals etc. Also if the temperature drops at night and they didn't have enough clothing, they could get hypothermia and so on.
I don't know anything about trees around there, maybe they're really short-lived? For forests around here, it's a gross overestimate.
Also, I bet it's not just the tree you're sleeping under that poses a risk, but also other trees in the vicinity that might fall on you. In a forest there are probably a bunch of trees "in range".
All of that said, I've often camped and slept in forests, as have many of my friends, and I've never heard of anyone being killed or injured by a falling tree, or ever heard or seen any "don't sleep under a tree, it might kill you"-advice, so I don't know...
I would still guess that the number is wrong, I suspect that trees have a longer life on average from becoming what we would consider "big" until they fall over. But it's still an important detail.
The logic in the story is BS. First, you would never, ever get into a car with that logic. Second, trees just aren't that ephemeral. People who live in a forest would be very aware of when they do or don't fall (or more problematically, drop large branches.) It's not as straightforward as just avoiding storms or dead-looking trees. Sustained wet weather, especially after a period of dry weather, is a common cause. As is the opposite for some trees (eg oak trees drop limbs in sustained hot dry weather.) As for disease or other causes, an experienced hunter in a familiar area could tell at a glance.
The message I got from the story is that they probably did have a very good reason. They either thought it would be too hard to communicate, or they were themselves cargo-culting the falling tree excuse when the reality was more likely to be... I dunno, snakes or nasty bugs or annoying sticky sap or whatever.
Like these guys probably have homes, with bedding of some sort, maybe they'd rather sleep next to their wives than some caterpillars. If I was giving somebody from a far-off place a tour of my workplace and, on the bus ride home, they suggested it was getting dark and we should camp on the sidewalk I'd probably not go for it. If they were really insistent I'd probably amplify the danger of sleeping on the sidewalk to shut them up.
The logic isn't applicable to any set of risks. As deadly as cars are, the risk of car crash death is much, much lower than 1/5000 per trip. It's probably closer to applicable to being a drunk driver, and "you would never operate a car drunk" is pretty accurate for many people.
When you hear once-in-a-hundred-year event, it makes it sound quite rare. One might look around and say (for example, in relation to climate) "why are so many of these happening?"
But it is unsurprising statistically. If you know just a thousand distinct geographic places, about 10 of them would experience such an event each year.
Of course there are other compounding factors. Not all events are one-in-one-hundred; some are one-in-ten, others are one-in-five-hundred, and with increasing scarcity by order of magnitude.
Another compounding factor is that the borders for "geographical areas" are fuzzy. Does a one-in-a-hundred year event that happens in Vermont also qualify as having happened in New Hampshire? Probably depends on the type and the specific measurements and the expectation according to history.
And what about aggregate events? E.g. "It's been five hundred years since we've seen this many tornadoes, which tend to happen once every ten years."
And in the opposite direction of the 100 square-inch plot of ground, there is blurring in the other direction: What do they mean in aggregate if they are fewer than expected at a global scale?
So my original comment was just a simple way of taking the average of all of these competing factors and stating a general truth that's somewhere in the middle. If you have X things that are expected to have 1/Y probabilities, then the probability of you experiencing any of them is not 1/Y, it's more like X/Y. (As in very likely.. that's more than 1 so obviously not mathematically correct.) But we often don't think about rare events this way - as no specific event being probable, but some collection of improbable events being almost certain. We perceive them all as 1/Y. It's just a very local way of thinking.
Of course in practice, it's quite hard to know whether conditions in one location are independent from another, or whether there's some degree of correlation or an underlying causal factor. This is why we have climate scientists.
"highest temperature ever recorded in town X" does not mean much.
"highest temperature recorded in country Y" on the other hand is more significant, especially if the country is large.
Any payoff from a trade which has even a very tiny probablity of making you go bust is zero.
Because once you go bust you are not going to be doing trading anymore.
He calls these Uncle Points.
They can and do drop large limbs at any time without warning.
There are gum trees in PNG.
https://www.nytimes.com/2013/01/29/science/jared-diamonds-gu...
There’s a term for this which I’m unable to recall and it’s not easy to Google. Would greatly appreciate if someone could help me out here!!
Probability of surviving a night under a tree is (1-1/5000).
15 years of hunting is roughly 5000 nights. The chances of never getting hit by a tree over that period are (1-1/5000) for each night, which compounds to
(1-1/5000)^5000 ≈ 1/e ≈ 1/2.7 < 0.4
Make it at least 5:1 for 30 years.
That's to say, at least 5 out of 6 hunters who sleep under a tree every day wouldn't survive doing it for 30 years.
And that, children, is why credit cards are a scary thing.
>>All these things are almost always true. But Heuristics That Almost Always Work tempt us to be more certain than we should of each
The most important thing to realize about risk is this:
The difference between [using knowledge, skill, technology, and planning to manage risk] vs. [getting away with something]
If you aren't managing risk, it is managing you. And you can get away with something for a long time, but it is always a matter of until you don't, and then it is too 'effin late for you.
When you are managing the risk, you can make an entire career or lifetime of doing things that will otherwise kill you in seconds, scuba diving, flying, mountaineering, building tall structures, working with molten metal or dangerous chemicals, etc., etc., etc.
You can also get away with very dangerous things for at least enough time to fool you into thinking you are smart. The article discusses this at length.
This is why when you need to understand and manage the risks, and also be very alert to close calls - they mean that even though you thought you're managing, you've actually gone into the land of [getting away with it], just saved by Pure Dumb Luck. Don't say "it's okay it worked", look at why, because you might not have as much PDL next time.
Back of the envelope calculation: Life expectancy of 72 years times 365 gives about 26k days, so your average chance of death on a given day is on the order of 1 in 26,000.
The tricks in this article might work in the short term. However, over the length of a career, it's difficult to outrun a negative reputation forever. Especially in the age of the internet, people will eventually catch on to what you're doing.
Each and every time you have a 1 in 5000 chance.
So you have 0.02% chance of getting crushed each time.
(1 - 0.0002)^10950 = 0.1119 1 - 0.1119 = 0.8881
So the probability of getting crushed at least one night over 30 years is ~89%
I think, my stats are from school over 10 years ago.
This is because, for example, if you flip a coin, each time you still only have 50% chance of getting tails. But the chances that you flip it 10 times in a row and never get heads are a lot less than 50%.
That's said. Another tricky bit is, what was measured when we said 1/5000 chance? This is where data can get confusing. Was that the odd of a tree falling at any given night? Or was that the odd of someone being crushed by a tree in their sleep at night? Or was it the odd of someone being crushed by a tree ever? Or the odd of a particular tree falling at night?
That's often where any prediction already begins to break down. For example, sorry to use the vaccines as an example, but when we say 90% efficacy, it means, out of x number of people who got a vaccine during the trial, 90% of those didn't get covid during some period, while in the placebo group it would be some other % who got it.
Reasoning about this already is tricky. You don't know the priors. What was the odd your participants were exposed to COVID? What if you'd measured over a longer period of time? What if that was just a lucky bunch?
>If you sleep under a tree 4999 times and nothing happens, that doesn’t mean the 5000 time you sleep under one you’ll get crushed.
No, but it means you have far more overall chances of getting crushed if you do it 5000 times, than if you do it once or twice.
The dusk was approaching, they were still in the forest and he proposed that they could sleep under a tree. The hunters were adamant in their refusal: no, this is dangerous, a tree might fall on you in your sleep and kill you. He relented, but silently considered them irrational, given that his assessment of a chance of a tree falling on you overnight was less then 1:5000.
Only later did he realize that for a lifelong hunter, 1:5000 are pretty bad odds that translate to a very significant probability of getting killed over a 30-40 year long hunting career.