Statistical Inference for Everyone • Hacker News Board

Re: Statistical Inference for Everyone

#11

Post by grayclhn » Fri, Nov 21, 2014, 2:18 AM UTC

ced wrote: post #10

grayclhn wrote: post #4

I haven't looked at it carefully, but it's hard to think of a setting where I'd want to teach from this book: it's aimed at stats 101 students, but uses python as the programming language (great language, but far beyond what I'd expect a typical intro stats student to be able to handle); it advocates bayesian statistics, which is a reasonable decision, but seems to take it to such an extreme that "hypothesis test" ne…

it advocates bayesian statistics, which is a reasonable decision, but seems to take it to such an extreme that "hypothesis test" never appears in the table of contents... That's not very unusual. It seems to follow the "logic of science" approach from Jaynes. Hypothesis testing is covered in chapters 4 and 6. Other books (Mackay, Jaynes, Murphy) only cover frequentist hypothesis testing to argue against it, so this i…

It's very unusual to not cover hypothesis testing in an introduction to statistics class. The students are going to see "testing" again. The passage you quoted was about teaching from the book, not using it for self study.

Re: Statistical Inference for Everyone

#12

Post by tlmr » Fri, Nov 21, 2014, 2:45 AM UTC

edtechdev wrote: post #9

Here are some more interactive and effective ways to learn stats online. The Open Learning Initiative's open Probability & Statistics Course out of Carnegie Mellon might just be the most researched and carefully designed course out there. http://oli.cmu.edu/courses/free-open/statistics-course-detai... Students learn more statistics concepts in half the time as a traditional stats course. http://oli.cmu.edu/get-to-kno…

What do you think of the CMU course vs this text book in question?

Re: Statistical Inference for Everyone

#13

Post by capnrefsmmat » Fri, Nov 21, 2014, 4:04 AM UTC

tlmr wrote: post #12

edtechdev wrote: post #9

Here are some more interactive and effective ways to learn stats online. The Open Learning Initiative's open Probability & Statistics Course out of Carnegie Mellon might just be the most researched and carefully designed course out there. http://oli.cmu.edu/courses/free-open/statistics-course-detai... Students learn more statistics concepts in half the time as a traditional stats course. http://oli.cmu.edu/get-to-kno…

What do you think of the CMU course vs this text book in question?

The CMU course is very traditional. It covers basic exploratory data analysis (summary statistics, plotting data), basic probability, and hypothesis testing and estimation. There's no programming, nothing Bayesian, and only brief discussion of regression.

(I taught 36-201, the intro stats course that was used to build the OLI course, this summer.)

Statistical Inference, on the other hand, seems to take a Bayesian perspective and is very much not your standard intro stats class. It looks interesting and I'll have to skim through some of it.

Re: Statistical Inference for Everyone

#14

Post by findjashua » Fri, Nov 21, 2014, 6:03 AM UTC

sebastianavina wrote: post #8

grayclhn wrote: post #4

I haven't looked at it carefully, but it's hard to think of a setting where I'd want to teach from this book: it's aimed at stats 101 students, but uses python as the programming language (great language, but far beyond what I'd expect a typical intro stats student to be able to handle); it advocates bayesian statistics, which is a reasonable decision, but seems to take it to such an extreme that "hypothesis test" ne…

The best book I've found about statistical inference is this one: http://www.amazon.com/exec/obidos/ASIN/188652923X/ref=nosim/... it comes with the bonus that you can take the full course (video lectures, recitations, assignments and quizes) on mit: http://ocw.mit.edu/courses/electrical-engineering-and-comput...

thanks for the links. I've been meaning to kick the tires and read up on some probability/stats stuff, and this seems like the perfect way to ease back into it. Bookmarked!

Re: Statistical Inference for Everyone

#15

Post by daniel-levin » Fri, Nov 21, 2014, 9:51 AM UTC

This book interesting because it forgoes the traditional approach of most mathematical statistics books. The preface states that it is done like this in order to avoid the "cookbook" approach taken by many statistics students. This is why it is ironic that "Bayes' Recipe" appears 15 times in this text, and on page 131 there is a five step algorithm for parameter estimation, and my favourite, oft-repeated, never explained recipe - "n > 30, you'll be fine". There is no mention of the CLT, MLE, method of moments estimation, biasedness of estimators, convergence in probability, how sampling distributions arise, or any of the theory of distributions that underpin all of the inferential procedures detailed in the book. I think that excluding these topics actually increases the cookbooky-ness of the text.

It is important that students understand the provenance of the inferential techniques they use so that they don't land up doing bogus science (which hurts the world) by not knowing the failure modes of these techniques. Of course not all students of statistics know the requisite mathematics to understand it all, at the very least put the failure modes into a cookbook form.

For the sake of science please don't ever do any inferential statistics without knowing when the method you're using works and when it breaks, what it is robust to, and what assumptions it makes. Statistics is really easy to break when used naively. The mathematics of statistics is not easy, and often results are highly counter-intuitive.

Re: Statistical Inference for Everyone

#16

Post by Sven7 » Fri, Nov 21, 2014, 10:37 AM UTC

daniel-levin wrote: post #15

This book interesting because it forgoes the traditional approach of most mathematical statistics books. The preface states that it is done like this in order to avoid the "cookbook" approach taken by many statistics students. This is why it is ironic that "Bayes' Recipe" appears 15 times in this text, and on page 131 there is a five step algorithm for parameter estimation, and my favourite, oft-repeated, never expla…

What books do you recommend?

Re: Statistical Inference for Everyone

#17

Post by eli_gottlieb » Fri, Nov 21, 2014, 11:26 AM UTC

ced wrote: post #10

grayclhn wrote: post #4

I haven't looked at it carefully, but it's hard to think of a setting where I'd want to teach from this book: it's aimed at stats 101 students, but uses python as the programming language (great language, but far beyond what I'd expect a typical intro stats student to be able to handle); it advocates bayesian statistics, which is a reasonable decision, but seems to take it to such an extreme that "hypothesis test" ne…

it advocates bayesian statistics, which is a reasonable decision, but seems to take it to such an extreme that "hypothesis test" never appears in the table of contents... That's not very unusual. It seems to follow the "logic of science" approach from Jaynes. Hypothesis testing is covered in chapters 4 and 6. Other books (Mackay, Jaynes, Murphy) only cover frequentist hypothesis testing to argue against it, so this i…

Whether the textbook author wants to preach the Way of Bayes or not, the students, provided they actually become empirical scientists, are going to face journal and conference reviewers who want to see p-values. Failing to teach them how to construct credible intervals and perform Bayesian significance testing based on posterior distributions is failing to teach them skills necessary for our profession.

Re: Statistical Inference for Everyone

#18

Post by hessenwolf » Fri, Nov 21, 2014, 12:51 PM UTC

daniel-levin wrote: post #15

This book interesting because it forgoes the traditional approach of most mathematical statistics books. The preface states that it is done like this in order to avoid the "cookbook" approach taken by many statistics students. This is why it is ironic that "Bayes' Recipe" appears 15 times in this text, and on page 131 there is a five step algorithm for parameter estimation, and my favourite, oft-repeated, never expla…

n greater than 30:

A quantity that follows normal distribution has two things to estimate, the variability of the quantity (standard deviation), and the mean. Both of these are estimated with uncertainty from a series of observations of the quantity (the data). The t-distribution allows us to make predictions, taking into account both sources of uncertainty for a normally distributed thing.

However, as the number of observations increases towards thirty, the estimate of the standard deviation gets really, really good, so you can happily ignore the uncertainty for that. Then you just need the normal distribution.

Re: Statistical Inference for Everyone

#19

Post by grayclhn » Fri, Nov 21, 2014, 2:27 PM UTC

hessenwolf wrote: post #18

daniel-levin wrote: post #15

This book interesting because it forgoes the traditional approach of most mathematical statistics books. The preface states that it is done like this in order to avoid the "cookbook" approach taken by many statistics students. This is why it is ironic that "Bayes' Recipe" appears 15 times in this text, and on page 131 there is a five step algorithm for parameter estimation, and my favourite, oft-repeated, never expla…

n greater than 30: A quantity that follows normal distribution has two things to estimate, the variability of the quantity (standard deviation), and the mean. Both of these are estimated with uncertainty from a series of observations of the quantity (the data). The t-distribution allows us to make predictions, taking into account both sources of uncertainty for a normally distributed thing. However, as the number of…

That is hugely misleading. It's only reasonable if the data are actually independent draws from a normal distribution. IRL, they're not.

Re: Statistical Inference for Everyone

#20

Post by bblais » Tue, Nov 25, 2014, 2:30 AM UTC

daniel-levin wrote: post #15

This book interesting because it forgoes the traditional approach of most mathematical statistics books. The preface states that it is done like this in order to avoid the "cookbook" approach taken by many statistics students. This is why it is ironic that "Bayes' Recipe" appears 15 times in this text, and on page 131 there is a five step algorithm for parameter estimation, and my favourite, oft-repeated, never expla…

"There is no mention of the CLT, MLE, method of moments estimation, biasedness of estimators, convergence in probability, how sampling distributions arise, or any of the theory of distributions that underpin all of the inferential procedures detailed in the book."

Lot's of good criticisms in this thread, which I'll have to look at. This one, however, is not. :) how many intro stats book, of the traditional kind, mention MLE, method of moments, biased vs unbiased estimators, etc...? None that I've seen. So, you're right, it becomes more "cookbooky" as a result, however, I would argue that all Bayes analysis follows the same recipe, whereas frequentist analysis typically follows many recipes - not obviously connected. It is that part that I criticize, not the fact that there is a recipe for doing things.