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Statistics: Losing Ground to CS, Losing Image Among Students

104 points by sampo 12y ago

east2west 12y ago
Talk about head in the sand. CS is subsuming stats (not really) because computers are a, if not the, major driving force begind statistical advances. L1 regulation became a fad in stats because computational optimization could efficiently solve this non-smooth optimization problem. Engineers are now advancing algorithms and moving it to real world use, while statisticians are still re-searching their field and finding applications using existing algorithms. There is nothing wrong with application, but this means that they are not at the forefront of research and more importantly they have to wait for other people to solve their problems because they are no capable of inventing novel algorithms. When the world at the large relies on computers for every little thing and on computer scientists for a lot of scientific advances, is it really surprising that computer scientists are more prominant? And computer scientists have plenty of rigor. NIPs conferrence is an AI/neuroscience conference that is now prestigious in stats as well. Some machine learning experts lack mathematical foundations but statisticians are not really good mathematicians either. Being able to regurgitate stat theorems and prove convergence in expection under two dozen conditions (no kidding; read average JASA papers) are not the hallmark of analytic rigor. Average stats Ph.D., even from top programs, lack thorough understanding of measure theory. Just look at standard stat textbooks; examples are aplenty (using statistical software), but proofs are nowher to be found. It is amazing how many statisticians are still using pseudo-inverses in numeric computation. The statistical education is a problem. The biggest factor being the very software this article is promoting: R. It is an atrocious monstrocity. Perhaps statisticians can live with it. Fine, but don't expect good programmers coming out of using R for five years. Python would be far better. In the end, statisticians are not professional programmers, the same way accountants are not. I don't see too many accountants fretting CS is subsuming their field. Let us not forget, most statistical methods have their roots in other science and engineering fields, and they are not particularly bothered.
- 1971genocide 12y ago
  I am just a undergrad maths student but here is my experience: I learnt more about bayesian statistics in my Machine Learning course than I did from my statistics module, reason ? The stat book/course is the most dull nonsense I ever came across even number theory made more sense to me than statistics, the whole point of the book/course was to proof some really abstract axiom which no one understood why they were doing it. Compare that to the process of writing a spam filter from scratch in MATLAB. A mathematical axiomatic system is only useful if you are solving some real problem using it, otherwise good luck living in the matrix. I have learnt that is far easier for mathematics to create garbage than to create useful ideas. The reason why I think stats sucks so much is because its a cultural problem in the community, If you google "how to write spam filter" I am sure someone made a wikihow with pictures to explain bayesian stats even to a snail. The reason is the computer science community constantly accepts their ignorance, even if a proof/program is trivial they will make sure to explain it. In the mathematics world no such thing exists. The big assumptions are "we leave it to the reader to proof this trivial fact" No Mr Smarty Pants, pls assume the reader is a 17 year girl who cannot send an email ( if you want to attract young people please understand their mindset first ). /end rant
  - GFK_of_xmaspast 12y ago
    Don't advertise your ignorance.
  - capnrefsmmat 12y ago
    Which stats book did you use for which course? Intro stats is typically taught abysmally, because it's been dragged down by eighty years of pre-computing historical stats.
    - 1971genocide 12y ago
      I was lucky to have studied in a british curriculum high school The books I used were these: http://www.amazon.co.uk/Edexcel-Level-Modular-Mathematics-Statistics/dp/0435519123 http://www.amazon.co.uk/Edexcel-Level-Modular-Mathematics-St... These books were amazing. they were about 100 pages with lots of color and worked example. They go from S1-S6. What I find amazing is I have seen some insane greek syndrome books explaining the same topics as these ""kiddee" colored books. However My statistics education stopped after college ( exception being khan academy videos ), I have replaced all my stats modules with CS modules in university, I have hacked the stats modules I was forced to do by mugging up formulas and memorizing exam techniques :( so you can see why this stats prof complaining ticked me off as he thinks students haven't tried and are acting in ignorance.
mturmon 12y ago
Here's another relevant HN discussion about the relationship of CS and Statistics: https://news.ycombinator.com/item?id=6630491 https://news.ycombinator.com/item?id=6630491 The boosting, random forest, support vector machine, and deep learning ideas should have been developed in Statistics. Why weren't they? It's much deeper than TI-83 calculators and cookbook AP courses!
- dthal 12y ago
  Boosting was, I suppose, invented by Schapire and Freund, both CS guys, but its most successful form, gradient boosting was invented by Friedman, a stat professor. Random forests were developed by Breiman and Cutler, both stats professors. SVM's probably weren't developed in statistics because they don't have a probability model.
  - mturmon 12y ago
    It sounds like you're challenging my central claim: conventional statistics has largely ignored the impact of computational technologies on inference, and dismissed the resulting failure as a mere marketing problem. You are wrong. Boosting is a generic technique, and reducing it to gradient boosting and then claiming it as "mainstream statistics" is silly. Boosting is not, even today, part of the standard statistics curriculum, despite its wide utility. But the bootstrap is. I wonder why? Describing Leo Breiman as a stats professor is similarly twisted. He was always an outlier in traditional statistics; in fact, he left a position in the math department at UCLA because he didn't fit in, and began independent consulting, during which time he developed CART. And during his time, years later, as a bona fide stats professor at UC Berkeley, he spent considerable effort overcoming a tendency of mainstream stats to ignore the implications of computing. For instance, he was one of a small number of stats professors who was active in the NIPS community. (His bio at http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290 http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290 is absolutely fascinating, see pages 195-6 for his feelings about conventional statistics.) And don't forget that a lot of the popularity of CART was due to the C4.5 implementation, due to Ross Quinlan, a CS professor with a physics background, which allowed easy building of classifiers on top of trees. Which points out another thing, that these advances have in many cases not come from, strictly speaking, computer science; physicists and engineers have been very influential. SVM's were developed by Vladimir Vapnik and collaborators, who came out of a Russian math/physics background. Who cares whether SVMs have a model or not? This is an example of the myopia that afflicts old-fashioned statistics. SVMs are highly effective predictors with deep mathematical properties and admit a statistical analysis of their performance and properties. (You don't think every statistician who uses logistic regression really believes this is a model for the data generation process! Yet logistic regression is part of the stats canon.) The SVM analysis ("large margin classifiers") largely took place, again, outside mainstream statistics. We could say the same thing about neural networks. And we could say the same thing about deep learning. NNs: 1980s-1990s. SVMs: 1990s-2000s. Deep learning: 2000s-2010s. In ten years, we'll be adding another example to the list, because this myopia is not just poor marketing skills.
anarchy99 12y ago
Tisk, tisk Norman. A good statistician shouldn't use anecdotal evidence with a sample size of one to support his/her points. If I picked up a random stat pub, I bet I could find some missing citations as well.
yiransheng 12y ago
In my opinion, one big reason in the fall of statistics is the asymmetry in curriculum. If you are in CS/Machine Learning, you will be forced/suggested to take a few statistics/probability courses, typically up to a regression course. These are good enough to get you started and familiar with statistical concepts and methodology. However, if you come from a typical statistics program, programming/computing courses are almost always never required. Professors in probability theory/statistics often do not have great computing skill, they might know enough to get around in R/Mathlab, and teach you the basics in these tools, but a lot of times you are left in the dark in terms learning how to code. If your career goal is an analyst, whose job is report oriented, you can get by by knowing just enough R (SAS, Matlab etc.) scripting. As your deliverables are generally fancy LaTex pdf (or presentations). But current job market pays and values more of a "data scientist", whatever that terms mean. This type of roles typically requires solid software engineering background, and requires you to be able to work with software developers and commit to the same codebase on a regular basis. The lack of programming/computing skills of a stats majored students will be a huge minus for this type of responsibilities.
- analog31 12y ago
  Granted, I started college 30+ years ago, so my perspective may be outdated, but I fear that it might not be. What you say about "asymmetry" struck me because it seems like a more widespread issue. Some anecdotes from my college years: * The CS students did all of their work on the college mainframe, and there was a pervasive attitude that personal computers were toys. * The earliest adopters of PC's, and of using computers to solve problems rather than as objects of study, were in the physics department. * Math required students to learn programming, but only one course used it -- numerical analysis. I wonder if doing symbolic math by hand still dominates math education. Of course I enjoy doing it as a form of recreation, but I think that my use of math in my subsequent career would be stunted, had I not taken an interest in programming outside of the math department. I also wonder if "the hot stuff in discipline A is being taught in department B" is just a feature of the chaotic nature of scholarship and research, meaning that a student needs to explore more than one field in order to really be top notch in their main chosen field.
- ibrahima 12y ago
iends 12y ago
My first stat class was an introductory statistics class that was geared towards biology students. It was pretty terrible. When I got into CS grad school I took more rigorous statistics courses and fell in love. I mean, head over heels in love. I tried to transfer out of the CS graduate school and into to the statistics graduate school. Because I would lose funding and would be starting over I instead decided just to quit when I was awarded my masters instead. (Now I just spend my days code JavaScript and CSS...what happened..) If I would have been exposed to statistics earlier in life I'm absolutely certain I would have majored in it in addition to computer science.
jostmey 12y ago
Statistics is taught wrong from the get go. Who cares about z-tables and chi values? It distracts from the important underlying laws of probability. Statistics has never been more important, and yet the vocabulary is badly outdated leaving students with a huge mountain of knowledge to climb before reaching anything of real utility.
- osazuwa 12y ago
  I don't quite agree, as the standard normal and Chi squared distribution are obviously important to understand. I think what you mean is that teaching stats based on hypothesis testing on simple experiments with small samples is not the best way to introduce students to this world. But the history of statistics was heavily influenced by designed experiments, while most of the data that is out there is observational. That is hard for older generations of statisticians to swallow.
- eric_bullington 12y ago
  > Who cares about z-tables and chi values? I think the problem is how these concepts are taught, not the concepts in and of themselves. Too many people leave their graduate-level stats courses (that they take for X scientific/engineering/sociological discipline) believing that 0.90, or 0.05, or 1 SD are somehow magical values. Too few students understand the concepts behind them (I had to work hard on my own to gain a glimpse into them).
Homunculiheaded 12y ago
I'm surprised that they didn't mention Leo Breiman's famous paper "Statistical Modelling: The Two Cultures"[0]. A worthwhile read for anyone interested in the topic. I could sum it up, but the abstract does a better job: ---------- Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theory and practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools. [0].http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Breiman_01_StatisticalModeling_TheTwoCultures.pdf http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Brei...
- east2west 12y ago
  Fantastic paper. I wonder what "irrelevant theory, questionable conclusions" he had in mind.
andyidsinga 12y ago
im currently reading "discovering statistics using R" ..even though we're using python and ipython at work - ipython is simply fantastic! -- hope to integrate the R support magic soon too.
noelwelsh 12y ago
After reading the title I spent some time thinking about what the causes might be before reading the article. To my great amusement the reasons I consider machine learning to be a more vital field than statistics were all given as "structural problems with the CS research “business model”". :) Take the great recent success of deep learning. This is a method that could have never arisen in statistics, because at this point in time it has no (well, very little) supporting theory. But it works! Given a decade or so we'll likely have a good theory but we shouldn't let lack theory hinder development of applications, and in machine learning we generally don't. The classic Brieman "Two Cultures" paper is very relevant here.
- 1971genocide 12y ago
  The irony is conventional scientists forgot was it was to be scientists ! the best science was created without knowing what you were creating.
viraptor 12y ago
I did three terms of statistics combined at two different universities and then took the EdX statistics course a few years later. I was not impressed with the way it's taught at any of those places - if that's the usual way to tech statistics, I'm not surprised it's not very popular. They say "This “engineering-style” research model causes a cavalier attitude towards underlying models and assumptions." - but CS should be easy to verify. Everything I ever did for the CS courses could be looked at and explained exactly why it's right in simpler terms than what I did in the first place. With statistics I get a feeling it's completely the other way around - you can get some data, answer a question and it takes really complicated explanation to prove what you did is right. If you do simple mistake like taking one tailed instead of two tailed test, you still get a result - but how can you actually test it's not the right one? I was never taught or could find any way to do that. In geometry you can fairly easily throw some more equations at the problem and see your solution matches them too. In CS you can prove some thing in multiple ways, or throw enough data at some algorithm that you can see that both results and complexity bounds are what you expect them to be. The statistics courses I've seen resulted in many people who can go through the usual assignments, but couldn't tell if what they got back was right or wrong.
lucidguppy 12y ago
I wonder if CS should be divided into heavy research and then a serious minor to be applied to other majors. CS of applications should augment other fields of study. CS of research should be the heavy duty stuff.
jtleek 12y ago
As an academic statistician running the largest data science program in the world (10,000+ have completed a class) I think that CS/Data Science only pose a threat to statistics if we don't adapt. I wrote about it here. http://simplystatistics.org/2013/04/15/data-science-only-poses-a-threat-to-biostatistics-if-we-dont-adapt/ http://simplystatistics.org/2013/04/15/data-science-only-pos...
murbard2 12y ago
I'm not sure what the difference between statistics and machine-learning is supposed to be, seriously. The theoretical foundation of any machine learning algorithm is probability theory and statistics. Is machine learning implied to be a more "hands on" approach, to focus more on learning a set of techniques than on learning the theoretical underpinning of such techniques? Is it basically "statistical engineering"? The least charitable interpretation would be that it's just a hipper term for it. This article pretty much sums up my impression of the divide: http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/ http://brenocon.com/blog/2008/12/statistics-vs-machine-learn...
cs702 12y ago
I disagree. Statistics is not "losing image" among students. It's the other way around: more and more students and software developers have become interested in statistics, inspired by the successes achieved by machine learning and AI researchers. Because machine leaning is statistical learning. It's almost impossible to build a machine learning system without learning at least some statistics! As evidence of the growing popularity of statistics, consider that this submission made the front page on HN. The "losing image" meme applies, not to the discipline of statistics, but to staid academic departments who have lost power and prestige. They don't like that their subject has become popular in a different department (CS), outside their purview. PS. I would add that many (but not all) statistics departments have brought this upon themselves. For a long time, their approach to real-world, practical applications has been to apply existing models in ways that can be readily interpreted by human beings, e.g., with "explanatory variables." Meanwhile, machine learning and AI researchers have been experimenting with, testing, and deploying new models and new ways of 'scaling up' old models, focused on making accurate predictions on previously-unseen data. Of course students find the latter more exciting.
moron4hire 12y ago
His attitude seems to be more "slow CS down" than "speed Stats up". He talks about "let's consider the problems in CS first", but then never gets around to seriously discussing the problems of the Stats profession. The most he seems to admit to is "Stats hasn't been awake, hasn't been present", like it's some kind of defendable excuse. It's a major problem! If you don't show up to the game, you can't say anything about the "competition" playing unfairly or not playing by the right rules or what have you. We've seen this before in all kinds of discussions. "Them damn kids and their Rubython on Djrails think they invented everything." It paints a picture of some new, young beast hunting down and devouring the old guard bit by bit in some kind of cruel game designed to torture body and mind. Which is completely ridiculous. The new gang never "attacks" the old. They just walk away. For all sets of tools, the reason people prefer one over another is because they are finding answers quicker and more easily with A rather than B. Nobody cares about the purity of form (or in other words, the ones who do care are statistically insignificant). They care about getting work done. None of this is to say that working on theory is inferior or superior to working on applied use and engineering. They are all equally valid pursuits. But we all need to be able to admit when people don't share our passions, and not blame them for having their own. Show up to the game. The game is industry. It's work. Stop focusing on academia as the only legitimate outlet for graduating mathematicians and statisticians. Talking about "adult supervision" is really just armchair quarterbacking. Sit in a meeting with a person with a 30-year-old, unused history degree with absolutely zero understanding of statistics--thinks ALL math is ivory tower bullshit not worth the paper the textbooks are printed on--and explain to him why his desired results can't be obtained from his current data. See how much your statistical models stand up to "let's turn that can't into a can" or "can't is unacceptable, we're not going to leave here until we figure it out".
kazinator 12y ago
I was expecting a statement like: "Based on a random sample of institutions surveyed, Statistics is losing ground to CS at a relative mean rate of 5.7% per year (by junior and senior year undergraduate enrollment headcounts, taken over the past five years from the institutions sampled), with a 95% confidence interval of +/- 0.3%." Ironically, no.

104 points by sampo 12y ago

Talk about head in the sand. CS is subsuming stats (not really) because computers are a, if not the, major driving force begind statistical advances. L1 regulation became a fad in stats because computational optimization could efficiently solve this non-smooth optimization problem. Engineers are now advancing algorithms and moving it to real world use, while statisticians are still re-searching their field and finding applications using existing algorithms. There is nothing wrong with application, but this means that they are not at the forefront of research and more importantly they have to wait for other people to solve their problems because they are no capable of inventing novel algorithms. When the world at the large relies on computers for every little thing and on computer scientists for a lot of scientific advances, is it really surprising that computer scientists are more prominant? And computer scientists have plenty of rigor. NIPs conferrence is an AI/neuroscience conference that is now prestigious in stats as well. Some machine learning experts lack mathematical foundations but statisticians are not really good mathematicians either. Being able to regurgitate stat theorems and prove convergence in expection under two dozen conditions (no kidding; read average JASA papers) are not the hallmark of analytic rigor. Average stats Ph.D., even from top programs, lack thorough understanding of measure theory. Just look at standard stat textbooks; examples are aplenty (using statistical software), but proofs are nowher to be found. It is amazing how many statisticians are still using pseudo-inverses in numeric computation. The statistical education is a problem. The biggest factor being the very software this article is promoting: R. It is an atrocious monstrocity. Perhaps statisticians can live with it. Fine, but don't expect good programmers coming out of using R for five years. Python would be far better. In the end, statisticians are not professional programmers, the same way accountants are not. I don't see too many accountants fretting CS is subsuming their field. Let us not forget, most statistical methods have their roots in other science and engineering fields, and they are not particularly bothered.

1971genocide 12y ago
I am just a undergrad maths student but here is my experience: I learnt more about bayesian statistics in my Machine Learning course than I did from my statistics module, reason ? The stat book/course is the most dull nonsense I ever came across even number theory made more sense to me than statistics, the whole point of the book/course was to proof some really abstract axiom which no one understood why they were doing it. Compare that to the process of writing a spam filter from scratch in MATLAB. A mathematical axiomatic system is only useful if you are solving some real problem using it, otherwise good luck living in the matrix. I have learnt that is far easier for mathematics to create garbage than to create useful ideas. The reason why I think stats sucks so much is because its a cultural problem in the community, If you google "how to write spam filter" I am sure someone made a wikihow with pictures to explain bayesian stats even to a snail. The reason is the computer science community constantly accepts their ignorance, even if a proof/program is trivial they will make sure to explain it. In the mathematics world no such thing exists. The big assumptions are "we leave it to the reader to proof this trivial fact" No Mr Smarty Pants, pls assume the reader is a 17 year girl who cannot send an email ( if you want to attract young people please understand their mindset first ). /end rant
- GFK_of_xmaspast 12y ago
  Don't advertise your ignorance.
- capnrefsmmat 12y ago
  Which stats book did you use for which course? Intro stats is typically taught abysmally, because it's been dragged down by eighty years of pre-computing historical stats.
  - 1971genocide 12y ago
    I was lucky to have studied in a british curriculum high school The books I used were these: http://www.amazon.co.uk/Edexcel-Level-Modular-Mathematics-Statistics/dp/0435519123 http://www.amazon.co.uk/Edexcel-Level-Modular-Mathematics-St... These books were amazing. they were about 100 pages with lots of color and worked example. They go from S1-S6. What I find amazing is I have seen some insane greek syndrome books explaining the same topics as these ""kiddee" colored books. However My statistics education stopped after college ( exception being khan academy videos ), I have replaced all my stats modules with CS modules in university, I have hacked the stats modules I was forced to do by mugging up formulas and memorizing exam techniques :( so you can see why this stats prof complaining ticked me off as he thinks students haven't tried and are acting in ignorance.

mturmon 12y ago

Here's another relevant HN discussion about the relationship of CS and Statistics: https://news.ycombinator.com/item?id=6630491 https://news.ycombinator.com/item?id=6630491 The boosting, random forest, support vector machine, and deep learning ideas should have been developed in Statistics. Why weren't they? It's much deeper than TI-83 calculators and cookbook AP courses!

dthal 12y ago
Boosting was, I suppose, invented by Schapire and Freund, both CS guys, but its most successful form, gradient boosting was invented by Friedman, a stat professor. Random forests were developed by Breiman and Cutler, both stats professors. SVM's probably weren't developed in statistics because they don't have a probability model.
- mturmon 12y ago
  It sounds like you're challenging my central claim: conventional statistics has largely ignored the impact of computational technologies on inference, and dismissed the resulting failure as a mere marketing problem. You are wrong. Boosting is a generic technique, and reducing it to gradient boosting and then claiming it as "mainstream statistics" is silly. Boosting is not, even today, part of the standard statistics curriculum, despite its wide utility. But the bootstrap is. I wonder why? Describing Leo Breiman as a stats professor is similarly twisted. He was always an outlier in traditional statistics; in fact, he left a position in the math department at UCLA because he didn't fit in, and began independent consulting, during which time he developed CART. And during his time, years later, as a bona fide stats professor at UC Berkeley, he spent considerable effort overcoming a tendency of mainstream stats to ignore the implications of computing. For instance, he was one of a small number of stats professors who was active in the NIPS community. (His bio at http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290 http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290 is absolutely fascinating, see pages 195-6 for his feelings about conventional statistics.) And don't forget that a lot of the popularity of CART was due to the C4.5 implementation, due to Ross Quinlan, a CS professor with a physics background, which allowed easy building of classifiers on top of trees. Which points out another thing, that these advances have in many cases not come from, strictly speaking, computer science; physicists and engineers have been very influential. SVM's were developed by Vladimir Vapnik and collaborators, who came out of a Russian math/physics background. Who cares whether SVMs have a model or not? This is an example of the myopia that afflicts old-fashioned statistics. SVMs are highly effective predictors with deep mathematical properties and admit a statistical analysis of their performance and properties. (You don't think every statistician who uses logistic regression really believes this is a model for the data generation process! Yet logistic regression is part of the stats canon.) The SVM analysis ("large margin classifiers") largely took place, again, outside mainstream statistics. We could say the same thing about neural networks. And we could say the same thing about deep learning. NNs: 1980s-1990s. SVMs: 1990s-2000s. Deep learning: 2000s-2010s. In ten years, we'll be adding another example to the list, because this myopia is not just poor marketing skills.

anarchy99 12y ago

Tisk, tisk Norman. A good statistician shouldn't use anecdotal evidence with a sample size of one to support his/her points. If I picked up a random stat pub, I bet I could find some missing citations as well.

yiransheng 12y ago

In my opinion, one big reason in the fall of statistics is the asymmetry in curriculum. If you are in CS/Machine Learning, you will be forced/suggested to take a few statistics/probability courses, typically up to a regression course. These are good enough to get you started and familiar with statistical concepts and methodology. However, if you come from a typical statistics program, programming/computing courses are almost always never required. Professors in probability theory/statistics often do not have great computing skill, they might know enough to get around in R/Mathlab, and teach you the basics in these tools, but a lot of times you are left in the dark in terms learning how to code. If your career goal is an analyst, whose job is report oriented, you can get by by knowing just enough R (SAS, Matlab etc.) scripting. As your deliverables are generally fancy LaTex pdf (or presentations). But current job market pays and values more of a "data scientist", whatever that terms mean. This type of roles typically requires solid software engineering background, and requires you to be able to work with software developers and commit to the same codebase on a regular basis. The lack of programming/computing skills of a stats majored students will be a huge minus for this type of responsibilities.

analog31 12y ago
Granted, I started college 30+ years ago, so my perspective may be outdated, but I fear that it might not be. What you say about "asymmetry" struck me because it seems like a more widespread issue. Some anecdotes from my college years: * The CS students did all of their work on the college mainframe, and there was a pervasive attitude that personal computers were toys. * The earliest adopters of PC's, and of using computers to solve problems rather than as objects of study, were in the physics department. * Math required students to learn programming, but only one course used it -- numerical analysis. I wonder if doing symbolic math by hand still dominates math education. Of course I enjoy doing it as a form of recreation, but I think that my use of math in my subsequent career would be stunted, had I not taken an interest in programming outside of the math department. I also wonder if "the hot stuff in discipline A is being taught in department B" is just a feature of the chaotic nature of scholarship and research, meaning that a student needs to explore more than one field in order to really be top notch in their main chosen field.
ibrahima 12y ago

iends 12y ago

My first stat class was an introductory statistics class that was geared towards biology students. It was pretty terrible. When I got into CS grad school I took more rigorous statistics courses and fell in love. I mean, head over heels in love. I tried to transfer out of the CS graduate school and into to the statistics graduate school. Because I would lose funding and would be starting over I instead decided just to quit when I was awarded my masters instead. (Now I just spend my days code JavaScript and CSS...what happened..) If I would have been exposed to statistics earlier in life I'm absolutely certain I would have majored in it in addition to computer science.

jostmey 12y ago

Statistics is taught wrong from the get go. Who cares about z-tables and chi values? It distracts from the important underlying laws of probability. Statistics has never been more important, and yet the vocabulary is badly outdated leaving students with a huge mountain of knowledge to climb before reaching anything of real utility.

osazuwa 12y ago
I don't quite agree, as the standard normal and Chi squared distribution are obviously important to understand. I think what you mean is that teaching stats based on hypothesis testing on simple experiments with small samples is not the best way to introduce students to this world. But the history of statistics was heavily influenced by designed experiments, while most of the data that is out there is observational. That is hard for older generations of statisticians to swallow.
eric_bullington 12y ago
> Who cares about z-tables and chi values? I think the problem is how these concepts are taught, not the concepts in and of themselves. Too many people leave their graduate-level stats courses (that they take for X scientific/engineering/sociological discipline) believing that 0.90, or 0.05, or 1 SD are somehow magical values. Too few students understand the concepts behind them (I had to work hard on my own to gain a glimpse into them).

Homunculiheaded 12y ago

I'm surprised that they didn't mention Leo Breiman's famous paper "Statistical Modelling: The Two Cultures"[0]. A worthwhile read for anyone interested in the topic. I could sum it up, but the abstract does a better job: ---------- Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theory and practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools. [0].http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Breiman_01_StatisticalModeling_TheTwoCultures.pdf http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Brei...

east2west 12y ago
Fantastic paper. I wonder what "irrelevant theory, questionable conclusions" he had in mind.

andyidsinga 12y ago

im currently reading "discovering statistics using R" ..even though we're using python and ipython at work - ipython is simply fantastic! -- hope to integrate the R support magic soon too.

noelwelsh 12y ago

After reading the title I spent some time thinking about what the causes might be before reading the article. To my great amusement the reasons I consider machine learning to be a more vital field than statistics were all given as "structural problems with the CS research “business model”". :) Take the great recent success of deep learning. This is a method that could have never arisen in statistics, because at this point in time it has no (well, very little) supporting theory. But it works! Given a decade or so we'll likely have a good theory but we shouldn't let lack theory hinder development of applications, and in machine learning we generally don't. The classic Brieman "Two Cultures" paper is very relevant here.

1971genocide 12y ago
The irony is conventional scientists forgot was it was to be scientists ! the best science was created without knowing what you were creating.

viraptor 12y ago

I did three terms of statistics combined at two different universities and then took the EdX statistics course a few years later. I was not impressed with the way it's taught at any of those places - if that's the usual way to tech statistics, I'm not surprised it's not very popular. They say "This “engineering-style” research model causes a cavalier attitude towards underlying models and assumptions." - but CS should be easy to verify. Everything I ever did for the CS courses could be looked at and explained exactly why it's right in simpler terms than what I did in the first place. With statistics I get a feeling it's completely the other way around - you can get some data, answer a question and it takes really complicated explanation to prove what you did is right. If you do simple mistake like taking one tailed instead of two tailed test, you still get a result - but how can you actually test it's not the right one? I was never taught or could find any way to do that. In geometry you can fairly easily throw some more equations at the problem and see your solution matches them too. In CS you can prove some thing in multiple ways, or throw enough data at some algorithm that you can see that both results and complexity bounds are what you expect them to be. The statistics courses I've seen resulted in many people who can go through the usual assignments, but couldn't tell if what they got back was right or wrong.

lucidguppy 12y ago

I wonder if CS should be divided into heavy research and then a serious minor to be applied to other majors. CS of applications should augment other fields of study. CS of research should be the heavy duty stuff.

jtleek 12y ago

As an academic statistician running the largest data science program in the world (10,000+ have completed a class) I think that CS/Data Science only pose a threat to statistics if we don't adapt. I wrote about it here. http://simplystatistics.org/2013/04/15/data-science-only-poses-a-threat-to-biostatistics-if-we-dont-adapt/ http://simplystatistics.org/2013/04/15/data-science-only-pos...

murbard2 12y ago

I'm not sure what the difference between statistics and machine-learning is supposed to be, seriously. The theoretical foundation of any machine learning algorithm is probability theory and statistics. Is machine learning implied to be a more "hands on" approach, to focus more on learning a set of techniques than on learning the theoretical underpinning of such techniques? Is it basically "statistical engineering"? The least charitable interpretation would be that it's just a hipper term for it. This article pretty much sums up my impression of the divide: http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/ http://brenocon.com/blog/2008/12/statistics-vs-machine-learn...

cs702 12y ago

I disagree. Statistics is not "losing image" among students. It's the other way around: more and more students and software developers have become interested in statistics, inspired by the successes achieved by machine learning and AI researchers. Because machine leaning is statistical learning. It's almost impossible to build a machine learning system without learning at least some statistics! As evidence of the growing popularity of statistics, consider that this submission made the front page on HN. The "losing image" meme applies, not to the discipline of statistics, but to staid academic departments who have lost power and prestige. They don't like that their subject has become popular in a different department (CS), outside their purview. PS. I would add that many (but not all) statistics departments have brought this upon themselves. For a long time, their approach to real-world, practical applications has been to apply existing models in ways that can be readily interpreted by human beings, e.g., with "explanatory variables." Meanwhile, machine learning and AI researchers have been experimenting with, testing, and deploying new models and new ways of 'scaling up' old models, focused on making accurate predictions on previously-unseen data. Of course students find the latter more exciting.

moron4hire 12y ago

His attitude seems to be more "slow CS down" than "speed Stats up". He talks about "let's consider the problems in CS first", but then never gets around to seriously discussing the problems of the Stats profession. The most he seems to admit to is "Stats hasn't been awake, hasn't been present", like it's some kind of defendable excuse. It's a major problem! If you don't show up to the game, you can't say anything about the "competition" playing unfairly or not playing by the right rules or what have you. We've seen this before in all kinds of discussions. "Them damn kids and their Rubython on Djrails think they invented everything." It paints a picture of some new, young beast hunting down and devouring the old guard bit by bit in some kind of cruel game designed to torture body and mind. Which is completely ridiculous. The new gang never "attacks" the old. They just walk away. For all sets of tools, the reason people prefer one over another is because they are finding answers quicker and more easily with A rather than B. Nobody cares about the purity of form (or in other words, the ones who do care are statistically insignificant). They care about getting work done. None of this is to say that working on theory is inferior or superior to working on applied use and engineering. They are all equally valid pursuits. But we all need to be able to admit when people don't share our passions, and not blame them for having their own. Show up to the game. The game is industry. It's work. Stop focusing on academia as the only legitimate outlet for graduating mathematicians and statisticians. Talking about "adult supervision" is really just armchair quarterbacking. Sit in a meeting with a person with a 30-year-old, unused history degree with absolutely zero understanding of statistics--thinks ALL math is ivory tower bullshit not worth the paper the textbooks are printed on--and explain to him why his desired results can't be obtained from his current data. See how much your statistical models stand up to "let's turn that can't into a can" or "can't is unacceptable, we're not going to leave here until we figure it out".

kazinator 12y ago

I was expecting a statement like: "Based on a random sample of institutions surveyed, Statistics is losing ground to CS at a relative mean rate of 5.7% per year (by junior and senior year undergraduate enrollment headcounts, taken over the past five years from the institutions sampled), with a 95% confidence interval of +/- 0.3%." Ironically, no.