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I remember how weird I felt in first year university taking frequentist based statistics. Unbeknownst to me, my statistics training in high school was completel

I remember how weird I felt in first year university taking frequentist based statistics. Unbeknownst to me, my statistics training in high school was completely Bayesian and when the university prof started talking about how we needed to sample a certain population set in order to come to an accurate conclusion I felt very uneasy with the whole mess. I know both systems are useful and reasonable in their own sense, and often even equivalent, but I found the Bayesian way of thinking so much more intuitive than the frequentist way.

icelancer 12y ago
I've taught a fair number of people statistics, and whatever they learn first is what is most intuitive - the vast majority of the time. It just so happens to be that they learn frequentist statistics (or some derivation of them) first.
eli_gottlieb 12y ago
I learned Bayesianism in a discrete probability and combinatorics class in undergrad, then took frequentist statistics in grad-school. The big problem is not that frequentism is somehow "just wrong", but that many people combine frequentist hypothesis testing with Popperian falsificationism (see rant above) to shoehorn frequentism into doing inference about hypotheses, which is profoundly wrong and not at all what frequentism is about, mathematically. The explanations for how frequentist inferences get used to do science feel wrong because they are: scientific induction is Bayesian. Of course, given mountains of data, either approach will work and converge to more-or-less identical results, but that's true of almost anything in statistics. Another thing is that I really wish the frequentist lectures would just up and admit that yes, likelihoods really are conditional probabilities: they're just conditional probabilities in the Bayesian mode. A likelihood (as used in frequentist statistics) is just the probability of the evidence conditioned on a certain parameter value. Even frequentists ought to admit that it talks about a counterfactual limit of the frequency ratio, but apparently calculations on counterfactuals are "too Bayesian" for them, so they pretend that "likelihoods aren't really probabilities". That confused the hell out of me in class.

icelancer 12y ago

I've taught a fair number of people statistics, and whatever they learn first is what is most intuitive - the vast majority of the time. It just so happens to be that they learn frequentist statistics (or some derivation of them) first.

eli_gottlieb 12y ago

I learned Bayesianism in a discrete probability and combinatorics class in undergrad, then took frequentist statistics in grad-school. The big problem is not that frequentism is somehow "just wrong", but that many people combine frequentist hypothesis testing with Popperian falsificationism (see rant above) to shoehorn frequentism into doing inference about hypotheses, which is profoundly wrong and not at all what frequentism is about, mathematically. The explanations for how frequentist inferences get used to do science feel wrong because they are: scientific induction is Bayesian. Of course, given mountains of data, either approach will work and converge to more-or-less identical results, but that's true of almost anything in statistics. Another thing is that I really wish the frequentist lectures would just up and admit that yes, likelihoods really are conditional probabilities: they're just conditional probabilities in the Bayesian mode. A likelihood (as used in frequentist statistics) is just the probability of the evidence conditioned on a certain parameter value. Even frequentists ought to admit that it talks about a counterfactual limit of the frequency ratio, but apparently calculations on counterfactuals are "too Bayesian" for them, so they pretend that "likelihoods aren't really probabilities". That confused the hell out of me in class.