Statistical Inference for Everyone
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Statistical Inference for Everyone
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Re: Statistical Inference for Everyone
#2Hopefully they can fix the embarrassing typo: "Monte Hall problem" should be "Monty." Not sure how that could have escaped notice. Maybe they were thinking about Monte Carlo simulations when writing that bit, but someone should have caught this.
Re: Statistical Inference for Everyone
#3Hopefully they can fix the embarrassing typo: "Monte Hall problem" should be "Monty." Not sure how that could have escaped notice. Maybe they were thinking about Monte Carlo simulations when writing that bit, but someone should have caught this.
If you read the citation they're following the original source, which isn't the common usage either, but at least it doesn't seem as glaring now.
http://www.jstor.org/discover/10.2307/2683689?uid=2&uid=4&si...
Re: Statistical Inference for Everyone
#4I 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" never appears in the table of contents...
But, it's obviously a labor of love and it's an interesting take on intro to stats. And, from skimming it, I don't see anything in it that's wrong. So this might be a good intro to bayesian stats for most HN readers.
edit: there is a wide range of quality for the graphs, though. Some look great, but some (the histograms especially) are... unappealing. And the formatting for the code sections is quite at odds with the style of the rest of the book. Those are minor, though.
second edit: not to start a license flamewar, but can this book be redistributed? It's licensed under either CC or GNU FDL, but I don't see a way to get the source code. So anyone hosting a copy would also need to license it under the FDL (since they can't remove the FDL licensing from the pdf), which they would then be violating. Am I understanding things correctly, or am I wrong?
Re: Statistical Inference for Everyone
#5Hopefully they can fix the embarrassing typo: "Monte Hall problem" should be "Monty." Not sure how that could have escaped notice. Maybe they were thinking about Monte Carlo simulations when writing that bit, but someone should have caught this.
Proofreading is one of the great unsolved technological problems. Human attention is a fantastically limited resource, and even multiple layers of checking frequently lets what subsequently appear to be "obvious" errors slip through.
The recent "cite crappy Whoever paper here" goof in a peer-reviewed journal is a typical example, and is notable only in that it is so egregious that it was caught and publicized. It is essentially certain that a large fraction of published papers contain at least one significant typo. I know of one case where two figures in a paper were identical (figure 2 was duplicated in figure 3) and it was missed by the co-authors (one of whom was fanatically careful) the journal editors and the referees.
We are never directly aware of our own inattentiveness, by definition, so the reality of how inattentive we are comes as a constant surprise.
To twist this vaguely back on topic: as well as being attentionally blind, we are also probability blind. I liken this to colour-blindness: we simply do not see probability distributions and have a terrible time thinking about them, yet we are completely immersed in them every day.
Between these two things--attentional blindness and probability blindness--we frequently end up interacting with the universe in ways that make little or no sense, as we behave as if we a) notice everything and b) live in a world of certain outcomes. The modern revolution of treating probability theory as logic is a huge big deal, and people who adopt it are likely to have a considerable advantage in years ahead. For one thing, it makes dealing with our attentional blindness easier, because it helps us understand and represent in our reasoning our imperfect attentional capabilities.
Re: Statistical Inference for Everyone
#6Hopefully they can fix the embarrassing typo: "Monte Hall problem" should be "Monty." Not sure how that could have escaped notice. Maybe they were thinking about Monte Carlo simulations when writing that bit, but someone should have caught this.
Proofreading is one of the great unsolved technological problems. Human attention is a fantastically limited resource, and even multiple layers of checking frequently lets what subsequently appear to be "obvious" errors slip through. The recent "cite crappy Whoever paper here" goof in a peer-reviewed journal is a typical example, and is notable only in that it is so egregious that it was caught and publicized. It is…
A clear link to the LaTeX source file on github would solve a large part of the proofreading problem. Especially since the book is nominally licensed under the GNU FDL.
Re: Statistical Inference for Everyone
#7Hopefully they can fix the embarrassing typo: "Monte Hall problem" should be "Monty." Not sure how that could have escaped notice. Maybe they were thinking about Monte Carlo simulations when writing that bit, but someone should have caught this.
If you read the citation they're following the original source, which isn't the common usage either, but at least it doesn't seem as glaring now. http://www.jstor.org/discover/10.2307/2683689?uid=2&uid=4&si...
The various spellings arise because the television host's real name was Monte Halparin, and he used the stage name "Monty Hall" [0].
Re: Statistical Inference for Everyone
#8I 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...
Re: Statistical Inference for Everyone
#9Here 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-know-oli/see-our-proven-results/
The Statistics Online Computational Resource (SOCR) site is also amazing for actually learning and playing with common statistical tests and tools: http://www.socr.ucla.edu/
Collaborative Statistics is a free and interactive statistics textbook: https://www.kno.com/book/details/productId/txt9780983804905
You can also run Sage, R, Python, Octave (Matlab clone) and other tools right in the browser now: https://cloud.sagemath.com/
Re: Statistical Inference for Everyone
#10I 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 is rather refreshing.