5 ms·

This goes beyond project estimations. The Metaculus tournaments have questions like > Will YouTube be banned in Russia before October 1? These are the kinds o

• tomlue 2y ago
  I know you're coming from a good place, but the 'most people I meet don't understand this' line about statistics is quite arrogant. Most people you meet are fully capable of understanding statistics; you should do a better job of explaining it when it comes up, or maybe you are the one who misunderstands. After all, most statisticians thought Marilyn vos Savant was wrong about the goats too...
  • robwwilliams 2y ago
    Not my experience at all. Just one example: try talking to physicians about false discovery rates; even those who do not profit from state-of-the art screening methods. Incorporating Bayesian methods is even a struggle for statisticians. It is a stuggle for me. John Ioannidis has much to say on this topic: https://en.wikipedia.org/wiki/John_Ioannidis https://en.wikipedia.org/wiki/John_Ioannidis
  • joebob42 2y ago
    My experience is that most people don't understand statistics and can be pretty easily mislead. That includes myself, with my only defense being that I'm at least aware statistics don't intuitively make sense and either ignore arguments based on them or if absolutely necessary invest the extra time to properly understand. Most people either don't realize this is necessary or don't have the background to do it even if they did, in my experience.
  • lordnacho 2y ago
    You don't have to be on the internet for long to see: - "Polls are useless, they only sampled a few thousand people" - "Why do we need the crime figures adjusted for the age/income/etc groups? Just gimme the raw truth!" Have to say, I think stats are the least well taught area in the math curriculum. Most people by far have no clue what Simpson's or Berkson's paradoxes are. Most people do not have the critical sense when presented with stats to ask questions like "how was the data collected" or "does it show what we think it shows". I just don't see it, tough ironically I don't have stats to back it up.
    • organsnyder 2y ago
      You don't have to be on the Internet long to see flat-earthers or any number of asinine ways of thinking. You can't stretch discrete observations from a supremely massive sample size into "most people".
      • ben_w 2y ago
        Gee, if only there was some kind of rigorous and well understood process for determining how to transform discrete observations according to how representative they are, such that we could build a model for a larger population. Something like that would be very useful for political decision, so perhaps we could name it after the latin word for "of the state"… ;P
        • thaumasiotes 2y ago
          > perhaps we could name it after the latin word for "of the state"… Civitatis?
          • ben_w 2y ago
            Alterum verbum est https://en.wiktionary.org/wiki/statistics#Etymology_1 https://en.wiktionary.org/wiki/statistics#Etymology_1
            • 2y ago
  • pessimizer 2y ago
    This is not at all true, and I think it's an example of what statisticians have to fight against in order to explain anything. Most people have an almost religious belief that inferences drawn from statistics should be intuitive, when they are often often extremely counterintuitive. > After all, most statisticians thought Marilyn vos Savant was wrong about the goats too... This is the opposite of the argument that you're making. Here you're saying that probability is so confusing and counterintuitive that even the experts get it wrong.
    • tomlue 2y ago
      My point was that the experts were blinded by arrogance. Even back then most ( almost all? ) statisticians were capable of understanding the monte hall problem. Yet they just assumed that a woman was wrong when she explained something that didn’t match their intuition. Instead of stopping to think, they let their arrogance take over and just assumed they were right.
    • deleted 2y ago
      [deleted]
  • kqr 2y ago
    It's definitely arrogant. But after much experience trying to explain these things to people, I'm more and more convinced it's not just "if you just put it the right way they will understand". Sure, they will nod politely and pretend to understand, and may even do a passable job of reciting it, but once its cast in a slightly different light they are just as confused. Much as with reading and writing, I think it takes an active imagination and a long slog of unlearning to trust logic (the "ought to" thinking that shields one from reality) and coming to terms with the race not being to the swift, etc, and that these effects can be quantified. It's not that some people are incapable of it. Much like literal literacy has reached rates of 99.9 % in parts of the world, I'm convinced statistical literacy can too. But when your teacher is not statistically literate (which I hypothesise they are not, generally speaking), they will not pass that on to you. They will not give you examples where the race is not to the swift. They will not point out when things seem to happen within regular variation and when they seem to be due to assignable causes. They will not observe the battle against entropy in seating choices in the classroom. They will not point out potential confounders in propensity associations. They will not treat student performance as a sample from a hypothetical population. They will not grade multiple-choice questions on KL divergence, although that would be far more useful. I could go on but I think you get the point. Yet to be clear, I'm not talking about just applying statistical techniques and tools. I'm talking about being able to follow a basic argument that rests on things like "yes I know they are a fantastic founder but startups fail 90 % of the time and so will they" or "if the ordinary variation between bus arrivals is 5–15 minutes and we have waited 20 minutes then there is something specific that put the bus we are waiting for into a different population." These are not obvious things to many people. This is not a personal failure – it is a lack of role models and teachers. I wouldn't have considered myself statistically literate until recently, and only thanks to accidentally reading the right books. I wouldn't even have known what I was missing were it not for that! I suspect it will take a few generations to really get it going. If someone would donate me large amounts of money I would love to actively research this subject, come up with reliable and valid scales to measure statistical literacy, and so on. But in the meantime I can only think in my spare time and sometimes write about it online.
    • shever73 2y ago
      What resources would you recommend for someone who wants to improve their statistical literacy? You mention reading the right books, I'd appreciate it if you could give a short list, if you have time.
  • nuancebydefault 2y ago
    I'm not surprised by the statement 'most people i meet don't understand this'. More than 50 percent of people I meet are less educated. Its statisticly evident. I might be over reaching but in fact what comes across as arrogance is just an example of statistical illiteracy. Most (>> 50 percent of) people are very good at detecting patterns. People are very bad at averaging numbers of events, because the detected patterns stand out so much and are implicitly and unconsciously exaggerated. An example. "in my city people drive like crazy" in fact means: this week i was a lot on the road and i saw 2 out of 500 cars that did not follow the rules and there was even one honking. It 'felt' like crazy traffic but in fact it was not.
  • michaelmarkell 2y ago
    The Monty hall problem is a great example of something I’ve been educated into believing, rationalizing, whatever you want to call it…but I would still never claim I “understand it.” I think that’s maybe the source of disagreement here, there are many truly unintuitive outcomes of statistics that are not “understood” by most people in the most respectful sense of the word, even if we’ve been educated into knowing the formula, knowing how to come to the right answer, etc. It’s like in chess, I know that the Sicilian is a good opening, that I’m supposed to play a6 in the najdorf, but I absolutely do not “understand” the Najdorf, and I do think it’s fundamentally past the limit of most humans understanding.
    • tomlue 2y ago
      you should totally spend some time thinking and experimenting with the monty hall problem then. It might not be as tricky as you think.
  • wisty 2y ago
    The vast majority of people are not that great at statistics. Even something as banal as "correlation is not causation, and can often be explained by a common cause" will blow a fair few minds (e.g. telling most people that old chestnut about how the murder rate is correlated with ice cream sales).
• dasil003 2y ago
  For forecasting with no agency involved this makes sense, but when you’re executing a project things get trickier. The hard part is finding appropriate reference classes to learn from while also not overlooking unique details that can sink your specific project.
• card_zero 2y ago
  > the Metaculus community does it again and again. Do they? I can't see where on that site there is something like "history of predictions" or "track record". Edit: here. https://www.metaculus.com/questions/track-record/ https://www.metaculus.com/questions/track-record/ But now I can't tell if it's good or bad.
  • kqr 2y ago
    The binary calibration diagram is what I would focus on. Of all the times Metaculus has said there's an x % of something happening, it has happened nearly exactly x % of the time. That is both useful and a little remarkable!
    • Retric 2y ago
      That doesn’t necessarily imply individual predictions are particularly good. If between two outcomes I say X wins 50% and I pick the winner randomly I’m going to be correct 50% of the time. However, if I offered people bets at 50/50 odds based on those predictions I would lose a great deal of money to people making more accurate predictions.
      • kqr 2y ago
        This is true! The Metaculus community forecasts also performs very well (in terms of score) in tournaments including other aggregation mechanisms though.
    • LegionMammal978 2y ago
      It also seems gameable: for every big question of societal importance that people care about for its own sake, have a thousand random little questions where the outcome is dead obvious and can be predicted trivially. Would you know anything talking about weighing questions to account for this?
      • christianqchung 2y ago
        Trivial questions wouldn't result in a good histogram where a probability of 30% actually results in something happening roughly 1 in 3 times. Trivial would mean questions where the community forecast is 1% or 99%. Those are not the vast majority of questions on the site. It would be very boring if the site was 70% questions where the answer is obviously yes or obviously no. Additionally, many questions require that you give a distributional forecast, in effect giving you 25/50/75th percentile outcomes for questions such as "how much will Bitcoin be with at the end of 2024?" Who would be gaming the system here anyway, the site? Individual users?
• krisoft 2y ago
  > Back in the 1500s shipping insurance was priced under the assumption that if you just learned enough about the voyage you could tell for certain whether it would be successful or not. Do you have a source on that? That sounds very unlikely to me. People just have to look at a single storm to see that it sometimes destroys some ships and does not destroy others. It is very clearly has a luck component. How could have anyone (at any time really) believe that "if you just learned enough about the voyage you could tell for certain whether it would be successful or not"?
  • kqr 2y ago
    Okay, that was an oversimplification. I have long wanted to write more on this but it never comes out right. What they did was determine the outcome with certainty barring the will of God or similar weasels. I.e. they did thorough investigations and then determined whether the shipment ought to be successful or not, and then the rest laid in the hands of deities or nature, in ways they assumed were incalculable. This normative perspective of what the future ought to be like is typical of statistical illiteracy. I'll get back to you on sources after dinner!
    • kqr 2y ago
      I think I got most of this from Willful Ignorance (Weisberg, 2014) but it was a while since I read it (and this was before I made more detailed notes) so I might be misremembering. There are what looks like fantastic books on the early days of probability and statistics aside from Weisberg (Ian Hacking is an author that comes to mind, and maybe Stephen Stigler?) but I have not yet taken the time to read them – I'll read more and write something up on this some day. But statistical understanding is such a recent phenomenon that we have been able to document its rise fairly well[1] which is fascinating in and of itself. [1]: And yet we don't know basic stuff like when the arithmetic mean became an acceptable way to aggregate data. Situations that obviously (to our eyes) call for an arithmetic mean in the 1630s were resolved some other way (often midrange, mode, or just a hand-picked observation that seemed somehow representative) and then suddenly in the 1680s we have this letter to a newspaper which uses the arithmetic mean as if it was the obvious thing to do and then its usage is relatively well documented after that. From what I understand we don't know what happened between 1630 and 1680 to cause this change in acceptance!