4 ms·
Interesting read. I don't think this came up in my stats classes: > Gosset solved many problems at the brewery with his new technique. The self-taught statisti
by mmastrac 2y ago
Interesting read. I don't think this came up in my stats classes:
> Gosset solved many problems at the brewery with his new technique. The self-taught statistician published his t-test under the pseudonym “Student” because Guinness didn’t want to tip off competitors to its research. Although Gosset pioneered industrial quality control and contributed loads of other ideas to quantitative research, most textbooks still call his great achievement the “Student’s t-test.”
- jbjbjbjb 2y agoI always thought that name was strange but I never thought to look it up. Stats books are so dry, they don’t have the inclination to share these kinds of stories.
- TillE 2y agoThe history of just about anything is very interesting, but it's generally not relevant in a textbook which has a specific purpose.
- mp05 2y agoI agree with the sentiment, but I always have wondered what t-test a real engineer uses and why they only teach the "Student" version. Given the context, a bit of a clarifier would have been appreciated.
- alpple 2y agoI wonder if dry reading means written without the influence of drink. I couldn't find an answer online. But, if so, it would be ironic to describe a stat book that ignored a brewer as dry.
- ayhanfuat 2y agoYou probably missed it. This is something stat book authors love to mention. I don’t remember a stat intro book that doesn’t have a footnote for “Student t”.
- BeetleB 2y agoJust went through mine a few months ago. It definitely doesn't have it.
- whimsicalism 2y agowhat book? i'm honestly curious because of how frequently this story is repeated in stats textbooks
- BeetleB 2y agoThe author is Jay Devore.
- nxobject 2y agoWait until you hear about the bad blood between Fisher and Pearson.
- jldugger 2y ago> Stats books are so dry, they don’t have the inclination to share these kinds of stories. It doesn't have to be that way. My pandemic lockdown read was a 10 dollar Stats textbook[1], that comes with tons of classic examples: the Salk polio vaccine, a prosecutor misusing the multiplication rule using purely circumstantial evidence ("what are the odds that police pulled over the wrong couple matching 10 different pieces of description by the victim?"), the classic Gallup poll showing FDR would defeat Landon (versus incumbent _Literary Digest_ showing a Landon win), Gosset's history with Guiness, the early history of probability as gambling strategy, a controversy over Mendel's data on pea plant heredity being _too_ clean, and so on. Sadly, while this book left me well prepared to apply statistical reasoning in my day job, it's departure from typical pedagogy left me feeling unprepared for further reading based on perusal of Stats Wikipedia -- what's a kernel? what's a moment? etc. [1]: https://www.amazon.com/Statistics-Fourth-David-Freedman-ebook/dp/B00SLB5Q72 https://www.amazon.com/Statistics-Fourth-David-Freedman-eboo...
- madcaptenor 2y agoIn a former life I taught some intro stat courses from this book. It’s a good book for an intro course for people who aren’t going on to further stat classes, although I think for a current class I’d want something that acknowledges how statistics and computers have gotten all tied up with each other. (I don’t have any recommendations - it’s not my job to know this any more.)
- deleted 2y ago[deleted]
- whimsicalism 2y agosorry but this is a classic story mentioned in almost every basic stats textbook i’ve read
- roenxi 2y agoDoubly unfortunate because the philosophical aspects of statistics are more important to students than most of maths. There are something like 4 different schools of thought [0] and people will have a natural propensity to one of them. Although they all agree on the formulas and rigorous aspects, it is actually a challenging proposition to comprehend what someone is doing if you strongly see the world from one perspective and don't realise that academics are potentially approaching the interpretation in one of 3 other ways. It adds a lot of dryness to the textbook because the author can really only talk about the objective parts in an introductory classroom setting. But if you're getting taught by a frequentist and have a subjectivity bent it is easy to spend a year or two confused before someone clues you in that there are unresolved questions of interpretation. [0] https://en.wikipedia.org/wiki/Interpretation_of_probability https://en.wikipedia.org/wiki/Interpretation_of_probability
- enasterosophes 2y ago> people will have a natural propensity to one of them. I see what you did there
- riffraff 2y agoInteresting, my university book of the subject was pretty tiny but it did talk of the different interpretations, but it only mentioned frequentist and bayesian. I did not suspect the story was much more complicated.
- deleted 2y ago[deleted]
- orhmeh09 2y agoI liked this one a lot Abelson, R. P. (1995). Statistics as Principled Argument. Psychology Press. https://www.routledge.com/Statistics-As-Principled-Argument/Abelson/p/book/9780805805284 https://www.routledge.com/Statistics-As-Principled-Argument/... > In this illuminating volume, Robert P. Abelson delves into the too-often dismissed problems of interpreting quantitative data and then presenting them in the context of a coherent story about one's research. Unlike too many books on statistics, this is a remarkably engaging read, filled with fascinating real-life (and real-research) examples rather than with recipes for analysis. It will be of true interest and lasting value to beginning graduate students and seasoned researchers alike. The focus of the book is that the purpose of statistics is to organize a useful argument from quantitative evidence, using a form of principled rhetoric. Five criteria, described by the acronym MAGIC (magnitude, articulation, generality, interestingness, and credibility) are proposed as crucial features of a persuasive, principled argument. Particular statistical methods are discussed, with minimum use of formulas and heavy data sets. The ideas throughout the book revolve around elementary probability theory, t tests, and simple issues of research design. It is therefore assumed that the reader has already had some access to elementary statistics. Many examples are included to explain the connection of statistics to substantive claims about real phenomena.
- mindcrime 2y agoAt least they let him publish, albeit under a pseudonym. It makes me wonder how many potentially useful discoveries were made in industrial settings, and wound up being buried due to management not wanting to risk leaking competitive information. The good news, I suppose, would be if you believe that it's rarely the case that only one person could ever discover something. Then you can conclude that all (most?) such discoveries were eventually (or will eventually be) rediscovered independently. On a related note... I wonder how much valuable research disappears (more or less) when companies fold, get acquired, etc. Take MCC[1] for example. I've been doing a lot of reading lately that involves old papers from the 1990's on "agents" and "multi-agent systems". And time and time again, in the references, you'll see something like "MCC Technical Report TR86-32791" or some-such. Occasionally said report can be found online, but quite a few of them seem to be either hard - or impossible - to find. Maybe there's an archive of physical papers stored away somewhere, but FSM knows where the heck such a thing would be, or how hard it would be to get access. A similar situation came up a while back when we started discussing "sharding" here on HN[2]. There was a lot of effort spent trying to identify when the term first arose, and a lot of evidence pointed to a particular paper that was internal to CCA, who were acquired by Xerox. And now that original paper seems to be unobtanium. The paper probably still exists somewhere in the bowels of Xerox, but good luck ever getting your hands on it. [1]: https://en.wikipedia.org/wiki/Microelectronics_and_Computer_Technology_Corporation https://en.wikipedia.org/wiki/Microelectronics_and_Computer_... [2]: https://news.ycombinator.com/item?id=36848605 https://news.ycombinator.com/item?id=36848605
- schneems 2y ago> It makes me wonder how many potentially useful discoveries were made in industrial settings, and wound up being buried due to management not wanting to risk Probably a lot. I’ve come to find out that some dinosaur companies won’t even let their programmers open up issues on open source repos (forget sending patches or releasing their own software). The logic goes like this: if someone found the log4j zero day before it was reported they could comb through all issues and see the companies that the users worked for then try to target them. In this case any comment would indicate possible involvement. The least bit of security, through the tiniest extra bit of obscurity. Thankfully many of these companies are starting to come around and realizing that a lack of involvement with open source is more risky than accidental 3rd hand information leaking (like what dependencies doesn’t certain company use).
- CrazyStat 2y agoThis is such a great story, it should be included in every intro stats class (I did, back when I taught intro stats). Gosset didn’t have the mathematical background to derive the correct distribution theoretically, so he figured out what it was by simulating drawing samples of different sizes thousands of times and fitting curves. Simulating, in those days, meant writing numbers on thousands of cards, then shuffling and drawing a sample. Calculate the mean and standard deviation. Repeat. Thousands of times. He published the result with an apologetic shrug for not being able to prove it properly.
- richrichie 2y agoalong with compulsory Guinness tasting :)
- ForOldHack 2y agoUm. Wow. That's quite a story. But, it's not real. "owever, Guinness had a policy of not publishing company data, and allowed Gosset to publish his observations on the strict understanding that he did so anonymously." I'm 1906, Gosset was the guest of Person at UCL, and since Gosset had a First in Math, and Professor Pearson was the leading mathematician and publisher of the Bell curve.. Gosset spent a year at UCL. University College London. A year with an expert looking over his shoulder? I would think that he would publish with an extreme amount of confidence, forgoing the need for an apolocetic shrug, which I have never ever heard of. Never, and I have a degree in math with a minor in Statistics. They had playing cards. You are arguing for large sample sizes, which is not economical - precisely against the design of the test - which looks surprisingly suspicious.
- zinekeller 2y ago> But, it's not real. "owever, Guinness had a policy of not publishing company data, and allowed Gosset to publish his observations on the strict understanding that he did so anonymously." Except that this part is true. Obviously, he is well-known in academic circles, but Guinness did have a policy against its employees to publish their research using a pseudonym[1]. [1] Specifically, they can publish with three conditions: 1) To not mention Guinness or its competitors, 2) To not mention anything about beer (so topics specifically about beer is forbidden), and 3) To not publish using their surname (which in practical effect is to publish using a pseudonym).
- petesoper 2y ago54 years after I was mystified trying to parse the use of "student" for this, here is the answer. Cool!
- squirrel6 2y agoThis was in my textbook and my professor covered it as well! Class of 17 here
- wodenokoto 2y agoI always found "student" confusing in the name. Like, is there a "professors t-test" or something? I personally found a lot of peace after learning that tidbit.
- jll29 2y agoMany international conferences are regularly held in Dublin, and attendees often visit the Guinness brewery as part of conferences' social events, where a memorial plaque reminds them of Gosset and his important contributions to statistics.