[HN Gopher] It Is Time to Stop Teaching Frequentism to Non-Stati...
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       It Is Time to Stop Teaching Frequentism to Non-Statisticians (2024)
        
       Author : Tomte
       Score  : 47 points
       Date   : 2025-05-24 17:27 UTC (5 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | NewsaHackO wrote:
       | It's weird how random people can submit non peer reviewed
       | articles to preprint repos. Why not just use a blog site, medium
       | or substack?
        
         | jxjnskkzxxhx wrote:
         | > Why not just use a blog site, medium or substack?
         | 
         | Because it looks more credible, obviously. In a sense it's
         | cargo cult science: people observe this is the _style_ of
         | science, and so copy just the style; to a casual observer it
         | appears to be science.
        
           | nickpsecurity wrote:
           | Professional science has been doing that a long time if one
           | considers that many published works were never independently
           | tested and replicated. If it's a scientist, and uses
           | scientific descriptions, many just repeat it from there.
        
             | jxjnskkzxxhx wrote:
             | Overly reductionistic. At the same time a proper rebuttal
             | isn't worth the time for someone who's clearly not looking
             | to understand.
        
         | billfruit wrote:
         | Why the gatekeeping. Only what is said matters, not who says
         | it.
        
           | BlarfMcFlarf wrote:
           | Peer review specifically checks that what is being said
           | passes scrutiny by experts in the field, so it is very much
           | about what is being said.
        
             | SJC_Hacker wrote:
             | They why isn't it double blind ?
        
               | BDPW wrote:
               | Often reviewing is executed double blind for exactly this
               | reason. This can be difficult in small fields where you
               | can more-or-less guess who's working on what, but the
               | intent is definitely there.
        
               | mcswell wrote:
               | I've reviewed computational linguistics papers in the
               | past (I'm retired now, and the field is changing out from
               | under me, so I don't do it any more). But all the reviews
               | I did were double blind.
        
           | tsimionescu wrote:
           | That's a cute fantasy, but it doesn't work beyond a tiny
           | scale. Credentials are critical to help filter data - 8
           | billion people all publishing random info can't be listened
           | to.
        
             | SoftTalker wrote:
             | > 8 billion people all publishing random info can't be
             | listened to.
             | 
             | Yet it's what we train LLMs on.
        
               | tsimionescu wrote:
               | It's what we train LLMs on to make them learn language, a
               | thing that all healthy adult human beings are experts on
               | using. It's definitely not what we train LLMs on if we
               | want them to do science.
        
               | birn559 wrote:
               | Which are known to be unreliable beyond basic things that
               | most people that have some relevant experience get right
               | anyway.
        
               | verbify wrote:
               | There's a paper Textbooks are all you need -
               | https://arxiv.org/abs/2306.11644
               | 
               | > We introduce phi-1, a new large language model for
               | code, with significantly smaller size than competing
               | models: phi-1 is a Transformer-based model with 1.3B
               | parameters, trained for 4 days on 8 A100s, using a
               | selection of ``textbook quality" data from the web (6B
               | tokens) and synthetically generated textbooks and
               | exercises with GPT-3.5 (1B tokens). Despite this small
               | scale, phi-1 attains pass@1 accuracy 50.6% on HumanEval
               | and 55.5% on MBPP. It also displays surprising emergent
               | properties compared to phi-1-base, our model before our
               | finetuning stage on a dataset of coding exercises, and
               | phi-1-small, a smaller model with 350M parameters trained
               | with the same pipeline as phi-1 that still achieves 45%
               | on HumanEval
               | 
               | We train on the internet because, for example, I speak a
               | fairly niche English dialect influenced by Hebrew,
               | Yiddish and Aramaic, and there are no digitised textbooks
               | or dictionaries that cover this language. I assume the
               | base weights of models are still using high quality
               | materials.
        
           | birn559 wrote:
           | If what is said has any merit can be very hard to judge
           | beyond things that are well known.
           | 
           | In addition, peer reviews are anonymous for both sides (as
           | far as possible).
        
             | ujkiolp wrote:
             | i would filter your dumb shit
        
           | watwut wrote:
           | Yeah, that is why 4chan became famous for being the source of
           | trustworthy and valuable scientific research. /s
        
           | jxjnskkzxxhx wrote:
           | > news.ycombinator.com/user?id=billfruit
           | 
           | > Why the gatekeeping. Only what is said matters, not who
           | says it.
           | 
           | Tell me you zero media literacy without telling me you have
           | zero media literacy.
        
         | groceryheist wrote:
         | Two reasons:
         | 
         | 1. Preprint servers create DOIs, making works better citable.
         | 
         | 2. Preprint servers are archives, ensuring works remain
         | accessible.
         | 
         | My blog website won't outlive me for long. What happened to
         | geocities could also happen to medium.
        
           | SoftTalker wrote:
           | Who would want to cite a random unreviewed preprint?
        
             | mitthrowaway2 wrote:
             | You don't get a free pass to not cite relevant prior
             | literature just because it's in the form of an unreviewed
             | preprint.
             | 
             | If you're writing a paper about a longstanding math problem
             | and the solution gets published on 4chan, you still need to
             | cite it.
        
               | NooneAtAll3 wrote:
               | tbf, you cite the paper that described and discussed said
               | solution in the more appropriate form
        
               | mousethatroared wrote:
               | You cite the form you encountered and if you're any good
               | of a researcher you will have encountered the original
               | 4chan anon post, Borges' short story, or Chomsky's
               | linguistic paper.
        
             | amelius wrote:
             | Maybe other pseudoscientists who agree with the ideas
             | presented and want to create a parallel universe with
             | alternative facts?
        
               | mousethatroared wrote:
               | And people who care more for gatekeeping will stick to
               | academic echo chambers. The list of community driven
               | medical discoveries encountering entrenched professional
               | opposition is quite long.
               | 
               | Both models are fallible, which is why discernment is so
               | important.
        
               | jononor wrote:
               | You can do that with reviewed papers too :)
        
             | bowsamic wrote:
             | It happens way more than you expect. In my PhD I used to
             | cite unreviewed preprints that were essential to my work
             | but simply for whatever reason hadn't been pushed to
             | publication. More common for long review like papers
        
             | jononor wrote:
             | Anyone who found something useful in it and are writing a
             | new paper.
             | 
             | That something is unreviewed does not mean that it is bad
             | or useless.
        
         | constantcrying wrote:
         | >It's weird how random people can submit non peer reviewed
         | articles to preprint repos.
         | 
         | It is weird how people use a platform exactly how it is
         | supposed to be used.
        
       | brudgers wrote:
       | Previous submission comments,
       | https://news.ycombinator.com/item?id=32341770
        
       | bmacho wrote:
       | Article is from 2012, compare [0] and [1].
       | 
       | The pdf got replaced for some reason (bug, sensitive information
       | in the meta or idk), but the article seems to have stayed the
       | same, except the date.
       | 
       | [0]: https://arxiv.org/pdf/1201.2590v1.pdf
       | 
       | [1]:
       | https://web.archive.org/web/0if_/https://arxiv.org/pdf/1201....
        
       | robwwilliams wrote:
       | Yes old, but even worse, it is not a well argued review. Yes,
       | Bayesian statistics are slowly gaining an upper hand at higher
       | levels of statistics, but you know what should be taught to first
       | year undergrads in science? Exploratory data analysis! One of the
       | first books I voluntarily read in stats was Mosteller and Tukey's
       | gem: Data Analysis and Regression. A gem. Another great book is
       | Judea Pearl's Book of Why.
        
         | wiz21c wrote:
         | Definitely. It always amazes me that in many situations, I'm
         | applying some stats algorithm just to conclude: let's look at
         | these data some more...
        
         | nxobject wrote:
         | On the subject of prioritizing EDA:
         | 
         | I need to look this up, but I recall in the 90s a social
         | psychology journal briefly had a policy of "if you show us
         | you're handling your data ethically, you can just show us a
         | self-explanatory plot if you're conducting simple comparisons
         | instead of NHST". That was after some early discussions about
         | statistical reform in the 90s - Cohen's "The Earth is round (p
         | < .05)" I think kick-started things off.
        
         | jononor wrote:
         | Yes. And the same for DS/ML people also, please. The amount of
         | ML people that can meaningfully drill down and actually
         | understand the data is surprisingly low sometimes. Even worse
         | for being able to understand a phenomena _using data_.
        
       | perrygeo wrote:
       | Frequentists stats aren't wrong. It's just a special case that
       | has been elevated to unreasonable standards. When the physical
       | phenomenon in question is truly random, frequentist methods can
       | be a convenient mathematical shortcut. But should we be teaching
       | scientists the "shortcut"? Should we be forcing every publication
       | to use these shortcuts? Statistic's role in the scientific
       | reproducibility crisis says no.
        
         | kccqzy wrote:
         | Frequentism methods are strictly less general. For example
         | Laplace used probability theory to estimate the mass of Saturn.
         | But with a frequentist interpretation we have to imagine a
         | large number of parallel universes where everything remains the
         | same except for the mass of Saturn. That's overly prescriptive
         | of what probability means. Whereas in Bayesian statistics what
         | probability means is strictly more general. You can manipulate
         | probabilities even without fully defining them (maximum
         | entropy) subject to intuitive rules (sum rule, product rule,
         | Bayes' theorem), and the results of such manipulation are still
         | correct and useful.
        
           | perrygeo wrote:
           | Drawing a sample of Saturns from an infinite set of Saturns!
           | It's completely absurd, but that's what you get when you take
           | a mathematical tool for coin flips and apply it to larger
           | scientific questions.
           | 
           | I wonder if the generality of the Bayesian approach is what's
           | prevented its wide adoption? Having a prescribed algorithm
           | ready to plug in data is mighty convenient! Frequentism
           | lowered the barrier and let anyone run stats, but more isn't
           | necessarily a good thing.
        
             | IshKebab wrote:
             | I dunno about you guys but I have no problems imagining
             | randomly sampling Saturn.
        
           | StopDisinfo910 wrote:
           | Laplace is typical use of inference statistics to built an
           | estimator. I don't really understand your point about
           | parallel universe here. It's absolutely not necessary for any
           | of the sampling to make sense. Every time you try to measure
           | anything, you are indeed taking a sample of the set of
           | measures you could have gotten given the tools you are using.
           | 
           | I fear you operate under the illusion that frequentist
           | statistics are somehow limited to hypothesis testing. It is
           | absolutely not the case.
        
         | wenc wrote:
         | Frequentist methods are unintuitive and seemingly arbitrary to
         | a beginner (hypothesis testing, 95% confidence, p=0.05).
         | 
         | Bayesian methods are more intuitive, and fit how most be reason
         | when they reason probabilistically. Unfortunately Bayesian
         | computational methods are often less practical to use in non-
         | trivial settings (usually involves some MCMC).
         | 
         | I'm a Bayesian reasoner, but happily use frequentist
         | computation methods (max likelihood estimation) because they're
         | just more tractable.
        
       | hnuser123456 wrote:
       | Okay, apparently this is the core of the debate?:
       | 
       | Frequentists view probability as a long-run frequency, while
       | Bayesians view it as a degree of belief.
       | 
       | Frequentists treat parameters as fixed, while Bayesians treat
       | them as random variables.
       | 
       | Frequentists don't use prior information, while Bayesians do.
       | 
       | Frequentists make inferences about parameters, while Bayesians
       | make inferences about hypotheses.
       | 
       | ---
       | 
       | If we state the full nature of our experiment, what we controlled
       | and what we didn't... how can it be a "degree of belief"? Sure,
       | it's impossible to be 100% objective, but it is easy to add
       | enough background info to your paper so people can understand the
       | context of your experiment and why you got your results. "we
       | found that at our college in this year, when you ask random
       | students on the street this question, 40% say this, 30% say
       | this..." and then considering how the college campus sample might
       | not fully represent a desired larger sample population... what is
       | different? you can confidently say something about the students
       | you sampled, less so about the town as a whole, less so about the
       | state as a whole...
       | 
       | I don't know, I finished my science degree after 10 years and
       | apparently have an even mix of these philosophies.
       | 
       | Would love to learn more if someone's inclined.
        
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       (page generated 2025-05-24 23:01 UTC)