4 ms·

As much as I appreciate and love all pedagogical endeavours in the field, especially in the form of open texts, I really, really, really dislike this overall ap

by epgui 3y ago

As much as I appreciate and love all pedagogical endeavours in the field, especially in the form of open texts, I really, really, really dislike this overall approach to teaching introductory statistics. I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. https://lindeloev.github.io/tests-as-linear/#:~:text=Most%20of%20the%20common%20statistical,most%20students%20know%20from%20highschool https://lindeloev.github.io/tests-as-linear/#:~:text=Most%20....

bschne 3y ago
I am partway through McElreath's "Statistical Rethinking" and I fully agree with this.
- epgui 3y ago
  That's a great textbook!
  - deleted 3y ago
    [deleted]
  - TheAlchemist 3y ago
    It's been recommended on this topic several times, so I'm looking at it. Quite expensive ! I see there is a series of lectures, which seems identical to the book. Is it the same ? Or still worth buying the book ?
    - noelwelsh 3y ago
      The lectures are good, and I've been told the book can be found online by the intrepid. I guess that Anna's Archive or Library Genesis has it.
      - TheAlchemist 3y ago
        I've found the book indeed - although it seems to be the first edition. It's here: https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources/RM-StatRethink-Bayes.pdf https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources...
        otteromkram 3y ago
        With all due respect, how hard did you look? This is the third result from a query I ran: https://github.com/Booleans/statistical-rethinking/blob/master/Statistical%20Rethinking%202nd%20Edition.pdf https://github.com/Booleans/statistical-rethinking/blob/mast... Main repo w/ some notebooks: https://github.com/Booleans/statistical-rethinking https://github.com/Booleans/statistical-rethinking
        TheAlchemist 3y ago
        Thanks !
- OrderlyTiamat 3y ago
  If you're starting out on statistical rethinking, be sure to check out recoded [0]: a reimplementation of statistical rethinkings exercises using tidyverse, ggplot and brms instead of McElreath's own package. These tools are standard and well worth learning on their own, and they're also more documented than the package made by McElreath. [0] https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/ https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/
begemotz 3y ago
I agree about teaching from a unified GLM basis. The 'bayesian-by-default' approach seems to going out on a more tenuous limb, imo.
- JHonaker 3y ago
  It's only appears tenuous because the subjective choices you have to make when using frequentist methods are made for you by the developer of the method. It's less comfortable to use Bayesian methods because you have to be explicit about your assumptions as the user, which opens your assumptions up for easier inspection. There's also way less specific information implied by priors than most people think. Informative priors should try to make distinctions between something that's reasonable-ish and something that's essentially infinity (take pharmacokinetics for example, the diffusion velocity of a molecule in your blood stream shouldn't have a velocity near the speed of light in a vacuum should it?). They should not be forcing your model to achieve a particular result. Luckily, because of the need to explicitly state them in a Bayesian analysis, it's much easier to determine if they were properly set. Prior specification is essentially problem domain-informed regularization where you can actually hope to understand if the hyperparameter is going to work or not.
fallat 3y ago
> I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. Is there anything where I can start today, as a guinea pig? My statistics education is basically zero.
- bschne 3y ago
  See my sibling comment, can recommend this: https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/
- noelwelsh 3y ago
  There are other comments here that suggests a number of books at varying levels. "Introduction to Modern Statistics" is very approachable in its presentation.
- trealira 3y ago
  There's this great series of lectures I watched during my intro to statistics and probability course: https://youtube.com/playlist?list=PLQfiOKXnQpw_l0rbiV_QW8lwlBpF_FX0a https://youtube.com/playlist?list=PLQfiOKXnQpw_l0rbiV_QW8lwl... It goes over Bayes' Theorem early on, which I assume is Bayesian-by-default. I didn't realize this isn't universal. I watched it because my professor seemed to be teaching from the same textbook, so it followed the same general course structure. The textbook we used was "Probability and Statistics for Engineering and the Sciences — 9th edition". You can find a PDF of the eighth edition just by googling the name. You could probably follow along just by taking notes during the video lectures, but if you want to give yourself homework, the textbook provides a lot of practice problems.
  - tnecniv 3y ago
    Bayes Theorem is universal. It’s one of the most fundamental results in probability. Bayesian statistics is not. Most basic intro courses go the frequentist routes for historical reasons, but really both methods have their pros and cons.
treszkai 3y ago
I understand where you're coming from, and I like the idea for a certain kind of people: those who are very good at handling abstractions. Software engineers do have this skill, but the majority of statistics users do not. Trying to explain the similarities between these linear methods and how all is one [1] to a social scientist who doesn't like numbers nor formulas to begin with would only lead to more confusion. But if you ever do a randomized test with a suitable linear model to estimate the efficacy of these two methods, do let us know, that would be 10/10 :) [1]: https://lindeloev.github.io/tests-as-linear/#41_one_sample_t-test_and_wilcoxon_signed-rank https://lindeloev.github.io/tests-as-linear/#41_one_sample_t...

by epgui 3y ago

bschne 3y ago

I am partway through McElreath's "Statistical Rethinking" and I fully agree with this.

epgui 3y ago
That's a great textbook!
- deleted 3y ago
  [deleted]
- TheAlchemist 3y ago
  It's been recommended on this topic several times, so I'm looking at it. Quite expensive ! I see there is a series of lectures, which seems identical to the book. Is it the same ? Or still worth buying the book ?
  - noelwelsh 3y ago
    The lectures are good, and I've been told the book can be found online by the intrepid. I guess that Anna's Archive or Library Genesis has it.
    - TheAlchemist 3y ago
      I've found the book indeed - although it seems to be the first edition. It's here: https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources/RM-StatRethink-Bayes.pdf https://civil.colorado.edu/~balajir/CVEN6833/bayes-resources...
      - otteromkram 3y ago
        With all due respect, how hard did you look? This is the third result from a query I ran: https://github.com/Booleans/statistical-rethinking/blob/master/Statistical%20Rethinking%202nd%20Edition.pdf https://github.com/Booleans/statistical-rethinking/blob/mast... Main repo w/ some notebooks: https://github.com/Booleans/statistical-rethinking https://github.com/Booleans/statistical-rethinking
        TheAlchemist 3y ago
        Thanks !
OrderlyTiamat 3y ago
If you're starting out on statistical rethinking, be sure to check out recoded [0]: a reimplementation of statistical rethinkings exercises using tidyverse, ggplot and brms instead of McElreath's own package. These tools are standard and well worth learning on their own, and they're also more documented than the package made by McElreath. [0] https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/ https://bookdown.org/ajkurz/Statistical_Rethinking_recoded/

begemotz 3y ago

I agree about teaching from a unified GLM basis. The 'bayesian-by-default' approach seems to going out on a more tenuous limb, imo.

JHonaker 3y ago
It's only appears tenuous because the subjective choices you have to make when using frequentist methods are made for you by the developer of the method. It's less comfortable to use Bayesian methods because you have to be explicit about your assumptions as the user, which opens your assumptions up for easier inspection. There's also way less specific information implied by priors than most people think. Informative priors should try to make distinctions between something that's reasonable-ish and something that's essentially infinity (take pharmacokinetics for example, the diffusion velocity of a molecule in your blood stream shouldn't have a velocity near the speed of light in a vacuum should it?). They should not be forcing your model to achieve a particular result. Luckily, because of the need to explicitly state them in a Bayesian analysis, it's much easier to determine if they were properly set. Prior specification is essentially problem domain-informed regularization where you can actually hope to understand if the hyperparameter is going to work or not.

fallat 3y ago

> I'm hoping to see, over time, a shift away from ad-hoc null hypothesis testing in favour of linear models (yes, in introductory courses, from the start-- see link below) and Bayesian-by-default approaches. Is there anything where I can start today, as a guinea pig? My statistics education is basically zero.

bschne 3y ago
See my sibling comment, can recommend this: https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/
noelwelsh 3y ago
There are other comments here that suggests a number of books at varying levels. "Introduction to Modern Statistics" is very approachable in its presentation.
trealira 3y ago
There's this great series of lectures I watched during my intro to statistics and probability course: https://youtube.com/playlist?list=PLQfiOKXnQpw_l0rbiV_QW8lwlBpF_FX0a https://youtube.com/playlist?list=PLQfiOKXnQpw_l0rbiV_QW8lwl... It goes over Bayes' Theorem early on, which I assume is Bayesian-by-default. I didn't realize this isn't universal. I watched it because my professor seemed to be teaching from the same textbook, so it followed the same general course structure. The textbook we used was "Probability and Statistics for Engineering and the Sciences — 9th edition". You can find a PDF of the eighth edition just by googling the name. You could probably follow along just by taking notes during the video lectures, but if you want to give yourself homework, the textbook provides a lot of practice problems.
- tnecniv 3y ago
  Bayes Theorem is universal. It’s one of the most fundamental results in probability. Bayesian statistics is not. Most basic intro courses go the frequentist routes for historical reasons, but really both methods have their pros and cons.

treszkai 3y ago

I understand where you're coming from, and I like the idea for a certain kind of people: those who are very good at handling abstractions. Software engineers do have this skill, but the majority of statistics users do not. Trying to explain the similarities between these linear methods and how all is one [1] to a social scientist who doesn't like numbers nor formulas to begin with would only lead to more confusion. But if you ever do a randomized test with a suitable linear model to estimate the efficacy of these two methods, do let us know, that would be 10/10 :) [1]: https://lindeloev.github.io/tests-as-linear/#41_one_sample_t-test_and_wilcoxon_signed-rank https://lindeloev.github.io/tests-as-linear/#41_one_sample_t...