21–30 of 138 posts
They should remove "modern" from the title, because who the hell uses the "R programming language" these days anymore?
Can I download this as a PDF? I'd like to read it offline.
I like the inclusion of randomization and bootstrapping. It's unfortunate that the hypothesis framework is still NHST -- I wouldn't consider that 'modern' by any means.
What is a good book on statistics that one can use for self-learning?
There can be a rather wide gap between a theoretical approach that you might encounter as taught by a statistician and an applied approach you might encounter in a business statistics or social science statistics course.
Depending on your math background and the area of intended application, in my opinion, it would sway recommendations for a first 'book' on statistics for self-learning.
I like the inclusion of randomization and bootstrapping. It's unfortunate that the hypothesis framework is still NHST -- I wouldn't consider that 'modern' by any means.
I don't see widespread agreement in the statistics community as to what should replace NHST. If you go Bayesian you need to completely rewrite the course. I've seen confidence intervals suggested as an alternative, but there are arguments against. I've also seen arguments that hypothesis tests shouldn't be used at all. Given that NHST is still widely used and there isn't a clear alternative I think it's a disservice…
And in that context, there is a lot of agreement that this approach is fundamentally flawed and outdated. if you are interested, I can provide references when I get to the office. But off the top of my head consider Gigerenzer and Cummings.
Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…
Statistics education is undergoing a bit of a revolution, driven by the accessibility of computers. For example, hypothesis testing is introduced by randomization[1], using a randomized permutation test[2]. I find this really easy to understand, compared to how I learned statistics using a more traditional approach. The traditional approach taught be a cookbook of hypothesis tests to use: use the t-test in this situa…
If you study mathematical statistics, it is not taught as a cookbook. At the elementary level you learn probability theory and distribution theory, all the different distributions, hypothesis tests, regression, ANOVA and so on proceed from there. Meanwhile, I think research scientists are often taught statistics as a set of recipes because its usually a short course for a specific discipline. E.g. Statistics for biologists.
Stats, imv, should be taught simulation-first: code up your hypotheses and see if they're even testable. Many many projects would immediately fail at the research stage.
Next, know that predictions are almost never a good goal. Almost everything is practically unpredictable -- with a near infinite number of relevant causes, uncontrollable.
At best, in ideal cases, you can use stats to model a distribution of predictions and then determine a risk/value across that range. Ie., the goal isnt to predict anything but to prescribe some action (or inference) according to a risk tolerance (risk of error, or financial risk, etc.).
It seems a generation of people have half-learned bits of stats, glued them together, and created widespread 'statistical cargo-cultism'.
The lesson of stats isnt hypothesis testing, but how almost no hypotheses are testable -- and then what do you do