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I've never heard anyone promote frequentist statistics. I've only ever heard Bayesians on the 'offense' talking about why their framework is better. To be clear
by mjfl 3y ago
I've never heard anyone promote frequentist statistics. I've only ever heard Bayesians on the 'offense' talking about why their framework is better. To be clear I am someone who doesn't really understand the faultlines.
- analog31 3y agoIndeed, when I took statistics in college, nearly 40 years ago, we proved Bayes theorem, and worked through several applications, so it could hardly have been regarded as controversial.
- adastra22 3y agoIt’s not. Statistics is statistics. This controversy is new and contrived.
- fbdab103 3y agoTell that to Ronald Fisher, who made it a point to deride Bayesian stats wherever there was an opportunity.
- jvans 3y agoThe controversy is decades old and largely resolved. Both methods work well on large enough data sets, Bayesian methods are far superior on small datasets
- adastra22 3y agoThis statement doesn’t really make sense to me. A frequentist approach assumes a random (or noisy) underlying process and characterizes its probability. A Bayesian approach assumes a fixed but unknown process and updates the probability a given model matches that reality. It is ultimately the same math, if you are comparing apples to apples. Just different ways of looking at a problem. Sometimes one is better suited than the other. It’s like as if physicists were arguing over whether Cartesian or Polar coordinates were better. It’s the same damn physics, just expressed differently. In some problems one approach is easier to work with than the other, and can even make seemingly intractable problems solvable. But that doesn’t mean the other approach was “wrong.”
- jvans 3y agoIn many cases yes. A simple example where this is not true is if your populations have different variances. T tests are no longer valid but heterogenous variances can be modeled with a Bayesian approach
- tnecniv 3y agoBayes theorem is fairly elementary probability. It isn’t the part that is questioned in Bayesian statistics. Someone disagreeing with Bayes theorem would be like disagreeing with addition. The controversial part is the methodology for statistical analysis built on top of it (e.g., you need a prior but where did that come from?)
- winwang 3y agoPriors come from the barrel of a gun :P
- Tomte 3y agoBayes theorem shares a name with Bayesianism, but it is not specific to it. No frequentist would question Bayes theorem, it is on the level of 1+1=2.
- SpaceManNabs 3y ago> I've never heard anyone promote frequentist statistics I will do it: 1. Computationally easier 2. often analytical theory available for most use cases so interpretability is high 3. more literature available so you can get unstuck faster if you mess up 4. no accusations of subjective bias in your prior (the con is clear, no ability to leverage subjective expertise) 5. In the asymptotic regime, MLE and bayesian MAP often converge anyways 6. king of hypothesis testing For most people, it doesn't matter. It matters when you are doing treatment for small sample sizes or other situations that would cause low power.
- bogtog 3y ago> king of hypothesis testing This advantage can't be understated. Researchers, at least in psychology, almost never seem to care about the magnitude of an effect. It's simply enough to show that some effect happens in some direction. For this (generally acceptable) purpose, frequentist stats are great.
- urschrei 3y agoA statistically significant result without something like a Hedges G (or Cohens D if comparing means) measure would be called out by even a moderately attentive reviewer as a matter of course (especially if small sample sizes are involved), and many reputable journals actually require effect sizes to be stated, so you run the risk of being desk rejected for leaving it out.
- nullc 3y agoFor leaving it out perhaps, but the result is publishable even if the effect is small... where for significant you usually must p-hack your way to a standard threshold. It's not entirely unreasonable, as often extra variables suppress the effect size. E.g. the effect is substantial in people with some genetics, insignificant otherwise. The fact that there is an effect at all makes it interesting as a starting point for further exploration e.g. to determine the mechanism or find the extra factors needed to make the effect significant.
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- zozbot234 3y agoFrequentist statistics is largely based on the maximum likelihood principle, which is a proper Bayesian analysis whenever the prior is a flat distribution. And since inference is mostly done on model parameters in frequentist statistics, the distribution you assume for them will also depend on how you parameterize the model. So it can often be justified as a convenient approximation to a "properly" Bayesian result.
- jdewerd 3y agoI've probably heard 1000 Bayesians rant about the alleged Frequentist consensus and 0 Frequentists complain about Bayesian analysis. I'd need some pretty steep priors to interpret this as anything other than the academic equivalent of "one WEIRD trick THEY don't want you to know" marketing.
- paulsutter 3y agoIf we were allowed one super-upvote per month, I would use mine right now on your comment
- selimthegrim 3y agoDang is going to file this under the Bumble model of HN monetization in his desk drawer
- causality0 3y agoThere are times I'd be willing to pay a fiver to un-flag my comment.
- jowea 3y agoOr just HN Gold
- throwawaymaths 3y agoThere's always room for a first time. Bayesian statistics is sound but I suspect it's often just used to justify biases. It is technically valid to use a prior and de facto never update it, because you know I'll get around to updating my prior next week, or... eventually, cough cough let's be honest, never
- marcosdumay 3y agoBayesian statistics is the one where you must state your bias explicitly, and justify them one by one under the light. It's the frequentist one that gets your biases implicitly, on the form of corrections and hypothesis formulation, so that people don't notice them.
- travisjungroth 3y agoFrequentist is the default. People don’t promote it. They just teach it as statistics, use p-values, and look funny at anyone who does otherwise. It’s like how you wouldn’t hear people in the US promote imperial measurements for home baking. When you dominate, you don’t have to advocate.
- analog31 3y agoI'd identify a larger problem, which is teaching anything as a bunch of recipes. My college offered stats in two tracks: There was a one-semester course for science majors, which was mostly plugging numbers into formulas. The course was utterly baffling for most of the students who took it. There was also a two-semester course for math majors. It was mainly about proofs, but also had time to go into more depth. You have to know the assumptions underlying a formula, if you're expected to prove it. ;-) But it was only taken by the math majors.
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- dan-robertson 3y agoI’m not sure that teaching mathstats more broadly is the answer to frequentist approaches being used by default, or to the recipe problem. I did the whole mathstats thing as a student and spent a bunch of time doing calculus around things like maximum likelihood estimators or unbiased estimators. I don’t think it’s very useful for the application of statistics in practice. I would recommend Statistical Rethinking by Richard McElreath as a course in Bayesian statistics targeted at scientists who want to extract correct, useful conclusions from their experiments, which doesn’t follow the “recipe book” pattern.
- analog31 3y agoI'm not sure of that either. For one thing, the students would be blown away. One thing we didn't have when I took stats was computers. I graduated from high school in 1982, and the colleges in my state were just beginning to get computers. I wonder if stats could be approached differently if it could start with nothing but data -- lots of it -- and graphing tools. This could even happen pre-college.
- mapmeld 3y agoI went to a week-long Probabilistic AI seminar, and I got the impression there was an era when enough stats and math professors did not take a Bayesian framework seriously, that there was a risk someone's thesis or dissertation defense would be derailed by someone on faculty.
- Avshalom 3y agoIt's worth pointing out that the vast majority of "Bayesians" you see talking shit have no actual training in bayesian statistics, or any statistics training beyond the college-intro level.
- terminous 3y ago> I've never heard anyone promote frequentist statistics. At least in my undergrad education, I took two dedicated statistics classes and many more domain science classes that used statistics, all of which were so steeped in the frequentist paradigm that frequentist vs Bayesian wasn't even mentioned. Frequentist statistics simply was statistics for me, until grad school.