## Launch party for CMU undergrad stats major programs

So here at CMU, we’re proud to have one of the “largest and fastest-growing” statistics departments in the US.

Tomorrow (March 3rd) is the launch party for several new (joint-)major programs for CMU undergrads: Statistics and Machine Learning, Statistics and Neuroscience, and Mathematical Statistics. That’s in addition to two existing programs: Statistics Core and the joint program in Economics and Statistics.

If you’re in Pittsburgh, come to the launch party at 4:30pm tomorrow. We’ll have project showcases, advising, interactive demos, etc., not to mention free food

## Journal bans null hypothesis testing and confidence intervals

So I’ve complained before about the problems with Null Hypothesis Significance Testing (NHST) and how, in many cases, it’d be more informative and more useful to report confidence intervals instead of p-values.

Well, the journal Basic and Applied Social Psychology has recently decided to ban p-values… but they’ve also tossed out confidence intervals and all the rest of classical statistical inference. And they’re not sold on Bayesian inference either. (Nor does their description of Bayes convince me that they understand it, with weird wordings like “strong grounds for assuming that the numbers really are there.”)

Apparently, instead of choosing another, less common inference flavor (such as likelihood or fiducial inference), they are doing away with rigorous inference altogether and only publishing descriptive statistics. The only measure they explicitly mention to prevent publishing spurious findings is that “we encourage the use of larger sample sizes than is typical in much psychology research, because as the sample size increases, descriptive statistics become increasingly stable and sampling error is less of a problem.” That sounds to me like they know sampling error and inference are important—they just refuse to quantify them, which strikes me as bizarre.

I’m all in favor of larger-than-typical sample sizes, but I’m really curious how they will decide whether they are large enough. Sample sizes need to be planned before the experiment happens, long before you get feedback from the journal editors. If a researcher plans an experiment, hoping to publish in this journal, what guidance do they have on what sample size they will need? Even just doubling the sample size is already often prohibitively expensive, yet it doesn’t even halve the standard error; will that be convincing enough? Or will they only publish Facebook-sized studies with millions of participants (which often have other experimental-design issues)?

Conceivably, they might work out these details and this might still turn out to be a productive change making for a better journal, if the editors are more knowledgeable than the editorial makes them sound, AND if they do actually impose a stricter standard than p<0.05, AND if good research work meeting this standard is ever submitted to the journal. But I worry that, instead, it'll just end up downgrading the journal's quality and reputation, making referees unsure how to review articles without statistical evidence, and making readers unsure how reliable the published results are.

See also the American Statistical Association’s comment on the journal’s new policy, and the reddit discussion (particularly Peter’s response).

Edit: John Kruschke is more hopeful, and Andrew Gelman links to a great paper citing cases of actual harm done by NHST. Again, I’m not trying to defend overuse of p-values—but there are useful and important parts of statistical inference (such as confidence intervals) that cannot be treated rigorously with descriptive statistics alone. And reliance on the interocular trauma test alone just frees up more ways to fiddle with the data to sneak it past reviewers.

## After 3rd semester of Statistics PhD program

It’s time for another braindump of reflections on statistics grad school.
See also the previous two posts: After 1st semester of Statistics PhD program and  After 2nd semester of Statistics PhD program.

This was my last semester of required coursework. Having passed the Data Analysis Exam in May, and with all the courses under my belt, I am pretty much ready to focus on the thesis topic search and proposal. Exciting!

## Dataclysm, Christian Rudder

In between project deadlines and homework assignments, I enjoyed taking a break to read Christian Rudder’s Dataclysm. (That’s right, my pleasure-reading break from statistics grad school textbooks is… a different book about statistics. I think I have a problem. Please suggest some good fiction!)

So, Rudder is one of the founders of dating site OkCupid and its quirky, data-driven research blog. His new book is very readable—each short, catchy chapter was hard to put down. I like how he gently alludes to the statistical details for nerds like myself, in a way that shouldn’t overwhelm lay readers. The clean, Tufte-minimalist graphs work quite well and are accompanied by clear writeups. Some of the insights are basically repeats of material already on the blog, but with a cleaner writeup, though there’s plenty of new stuff too. Whether or not you agree with all of his conclusions [edit: see Cathy O’Neil’s valid critiques of the stats analyses here], the book sets a good example to follow for anyone interested in data- or evidence-based popular science writing.

Most of all, I loved his description of statistical precision:

Ironically, with research like this, precision is often less appropriate than a generalization. That’s why I often round findings to the nearest 5 or 10 and the words ‘roughly’ and ‘approximately’ and ‘about’ appear frequently in these pages. When you see in some article that ‘89.6 percent’ of people do x, the real finding is that ‘many’ or ‘nearly all’ or ‘roughly 90 percent’ of them do it, it’s just that the writer probably thought the decimals sounded cooler and more authoritative. The next time a scientist runs the numbers, perhaps the outcome will be 85.2 percent. The next time, maybe it’s 93.4. Look out at the churning ocean and ask yourself exactly which whitecap is ‘sea level.’ It’s a pointless exercise at best. At worst, it’s a misleading one.

I might use that next time I teach.

The description of how academics hunt for data is also spot on: “Data sets move through the research community like yeti—I have a bunch of interesting stuff but I can’t say from where; I heard someone at Temple has tons of Amazon reviews; I think L has a scrape of Facebook.

Sorry I didn’t take many notes this time, but Alberto Cairo’s post on the book links to a few more detailed reviews.

## “Statistical Modeling: The Two Cultures,” Breiman

One highlight of my fall semester is going to be a statistics journal club led by CMU’s Ryan Tibshirani together with his dad Rob Tibshirani (here on sabbatical from Stanford). The journal club will focus on “Hot Ideas in Statistics“: some classic papers that aren’t covered in standard courses, and some newer papers on hot or developing areas. I’m hoping to find time to blog about several of the papers we discuss.

The first paper was Leo Breiman’s “Statistical Modeling: The Two Cultures” (2001) with discussion and rejoinder. This is a very readable, high-level paper about the culture of statistical education and practice, rather than about technical details. I strongly encourage you to read it yourself.

Breiman’s article is quite provocative, encouraging statisticians to downgrade the role of traditional mainstream statistics in favor of a more machine-learning approach. Breiman calls the two approaches “data modeling” and “algorithmic modeling”: Continue reading

## After teaching 1st statistics course

I’ve just finished an exhausting but rewarding 6 weeks teaching a summer-session course on “Experimental Design for Behavioral and Social Sciences,” CMU course 36-309. My course materials are secreted away on Blackboard, but here is my syllabus. You can also see some materials from a previous session here, including Howard Seltman’s textbook (free online).

The students were expected to have already taken an introductory statistics course. After a short review of basic concepts and t-tests, we dove into more intermediate analyses (ANOVA and regression, contrasts, chi-square tests and logistic regression, repeated measures) and into how a good study should be designed (power, internal/external validity, etc.)

I’ve taught one-off statistics workshops before, and I’ve taught once-a-week semester-long Polish language classes, but this was my first experience teaching a full-length course in statistics. Detailed notes are below.

## What the Best College Teachers Do, Ken Bain

Although CMU has no school of education, it does have strong support for those of us who’d like to become better educators, not just better researchers. There’s the Eberly Center, which bridges the research-about-education that happens on campus, to the education-of-researchers for which most of us are here. And there’s the brand-new Simon Initiative—I’m not fully sure yet what it entails, but I enjoyed the inaugural lecture by Carl Wieman on improving science education.

Amidst all this, I’ve started teaching a summer course (36-309, Experimental Design). While preparing to teach, I’ve read Ken Bain’s What the Best College Teachers Do (recommended by CMU’s Sciences Teaching Club).

Much of the content is about convincing you to adopt the mindset of a good teachers: You should be interested in the students’ understanding, not just in getting them to regurgitate facts or plug & chug formulas. You should be patient with learners of different types and levels. Assessments for the sake of getting feedback should be frequent and separate from assessments for the sake of labeling the student with a final grade. You want the students to become able to learn independently, so train them to think constructively about their own learning.

Mostly, this is stuff I already agreed with. I really like Bain’s high-level ideas. But I wish there would have been more concrete illustrations of how these ideas work in practice. Practical examples could have replaced a lot of the fluffy language about the opening the students’ minds and hearts, etc.

Still, there are a couple of lists of explicit questions to use when planning your course. No list can cover everything you need to consider—but still, it doesn’t hurt to use such a list, to ensure that at least you haven’t overlooked what’s on it.

Bain also has some lists of “types of learners” or “developmental stages of learning.” It’s often unhelpful to pigeonhole individual students into one bucket or another… but it can be useful to treat these archetypes as if they were user personas, and consider how your lesson plan will work for these users.

Some of these lists, and other excessive notes-to-self, below the break.

## How to Listen to and Understand Great Music, Robert Greenberg

These are just notes to myself on an audio course I got from the library. Nothing about statistics or R here

I’ve spent the past few months listening to Robert Greenberg’s How to Listen to and Understand Great Music, 3rd Edition as I walk to and from school. I’ve played classical music for years (in school bands and orchestras as well as at home), so I’d picked up a fair bit about its history, but I hoped this survey course would fill in some gaps.

Below are some notes-to-self, though my appetite for note-taking got weaker and eventually petered out halfway through the course.

## Winter is coming (to the Broad Street pump)

We live in an amazing future, where an offhand Twitter joke about classic data visualizations and Game of Thrones immediately turns into a real t-shirt you can buy.

Hats off to Alberto Cairo (whose book The Functional Art and blog are the best introductions to data visualization that I can recommend—but you already knew that).

If you don’t already know the story of John Snow and the Broad Street pump—or if you think you do but haven’t heard the full details—then The Ghost Map is a great telling.

Update: Alberto continues to kick this up a notch, adding two more Game Of Thrones-themed classic dataviz jokes, and making the images/captions available under the Creative Commons license. Awesome.

## After 2nd semester of Statistics PhD program

Here’s another post on life as a statistics PhD student (in the Department of Statistics, at Carnegie Mellon University, in Pittsburgh, PA).
The previous such post was After 1st semester of Statistics PhD program.

Classes:

• I feared that Advanced Probability Overview would be just dry esoteric theory, but Jing Lei ensured all the topics were really well-motivated. Although it was tough, I did better than I’d hoped (especially given that I’ve never taken a proper Real Analysis course). In Statistical Machine Learning, Larry Wasserman and Ryan Tibshirani did a great job of balancing “old” core theory with new cutting-edge research topics, including helpful homework assignments that gave us practice both in theory and in applications.
• My highlight of the semester was being able to read and digest a research paper that was way too abstract when I tried reading it a few years ago. It really hit me that I must be learning something in grad school
(The paper was Building Consistent Regression Trees from Complex Sample Data, by Toth and Eltinge. While working at Census, I wanted to try running a complex-survey-weighted regression tree, but I couldn’t get much out of this paper. Now, after a good dose of probability theory and machine learning, it’s far clearer. In fact, I have some ideas about extending this work!)
• The Statistical Machine Learning class referenced a ton of crazy math terms I wasn’t familiar with: Banach and Hilbert spaces, Lp norms, conjugate functions, etc. It terrified me at first—I’ve never even heard of this stuff, should I have taken grad-level functional analysis before I started this PhD, am I about to fail?!?—but it turns out a lot of it is just names for specific versions of general concepts that I already knew. Whew. Also, most of it got used repeatedly from topic to topic, so we did gain familiarity even without explicitly taking a functional analysis course etc. So, don’t get disheartened too easily by unfamiliar terminology!
• It was great to finally learn more about Lp norms and about splines. Also, almost everything in SML can be written as a penalized regression 😛
• Smoothing splines and Reproducing Kernel Hilbert Space (RKHS) regression are nifty because the setup is that you want to optimize over all possible functions. So you start out with an infinite-dimensional space, for which in general there might be no simple way to search/optimize! … But in these specific setups, we can prove that the optimal solution happens to lie in a finite-dimensional subspace, where your usual optimization/search tools will work after all. Nice.
• Larry had a nice “foundations” day in SML, with examples where Bayes and Frequentist analysis differ greatly. However, I didn’t find most of his examples too convincing, since the Bayesian “loses” only due to a stupid choice of priors; or the Bayesian “loses” for finite n but in a case where n in practice would have to be ridiculously large. Still, this helped stretch my thinking about how these inference philosophies differ.
• Larry points out: you often hear that “We might as well go Bayes because if you give people a Frequentist interval, they’ll interpret it as a Bayes interval.” But the reverse is also true: Give someone a sequence of 95% Bayes intervals, and they’ll expect 95% of them to contain the true value. That is NOT necessarily going to happen with Bayes CIs (unlike Frequentist CIs).
• In addition to Subjective, Objective, Empirical, or Calibrated Bayes, let me propose “Cynical Bayes”: Don’t choose a prior because you believe it. Instead, choose one to optimize your estimator’s Frequentist properties. That way you can keep your expert Freq’ist colleagues happy, yet still call it a Bayes estimator, so you can give the usual Bayes interpretation to keep nonexperts happy
• A background in Statistics will keep you thinking about distributions and probabilities and convergences. But a background in Applied Math may be better at giving you tools and ideas for feature engineering. It’s worth having both toolsets.
• The Advanced Probability Overview course covered some measure-theoretic probability. I’m finally understanding the subtleties of how the different convergences $\xrightarrow{p}$, $\xrightarrow{as}$, $\xrightarrow{D}$, and $\xrightarrow{L^p}$ all differ, and why it matters. We saw these concepts last semester in Intermediate Statistics, but the distinctions are far clearer to me now.
• AdvProb’s measure theory section also really helped me understand why textbooks say a random variable is a “function”: intuitively it seems like just a variable or a number or something… but in fact it really is a function, from “the state of the world” i.e. an element $\omega$ of the set $\Omega$ of all possible outcomes or states of the world, to the measurement you will collect (often a number on the real line). Finally, this measure theory view of probability, as the size of a subset of $\Omega$, is helpful. Even though statisticians’ goal is to develop tools that let them work with the range of the random variable and ignore the domain $\Omega$, it’s good to remember that this domain exists.
• However, measure theory and probability theory suffer from some really poor terminology! For example, it took me far too long to realize that “integrable” means “the integral is finite”, NOT “the integral exists.”
• When we teach students R, we really should use practical examples, not the arbitrary generic examples that you see so often. Instead of just showing me list(1,"a"), it helps to give a realistic example of why you may actually need to collect together numeric and character elements in a single object.

Research:

• I started a new research project, the Advanced Data Analysis project, which will run until the end of this upcoming Fall semester (so about a year total). I am working with Rob Kass and Avniel Ghuman on using magnetoencephalography (MEG) data to study epilepsy.
• At Rob’s research group meetings, I learn a ton from the helpful questions he asks. When presenting someone else’s work (i.e. for a journal club), ask yourself, “What would you do if *your* research was based on the data from this paper?” Still, I’ve found I really do need to keep scheduling weekly 1-on-1 meetings—the group meetings are not enough to stay optimally on track.
• Neuroscience is hard! Pre-processing massive neuroscience datasets using not-fully-documented open source software is particularly hard. When I chose this project, I did not realize how much time I would have to spent on learning the subject matter, relevant specialized software tools, and data pre-processing workflow. Four months in and I’ve still barely gotten to the point of doing any “real” statistics. It’s a good project and I’m learning a lot, but it’s disheartening to see how much of that learning has been tied to debugging open-source software installations that I’ll only ever use again if I stay in this sub-field.
I would advise the next PhD cohort to choose projects that’ll primarily teach you more general-purpose, transferable skills. Maybe take an existing theoretical method that’s not implemented in software yet, and make it into an R package?

Life:

• This was a tougher semester in many ways, with harder classes and more research-related setbacks. The Cake song Tougher than it is got a lot of play time on my headphones 😛
• I’m glad that despite my slow posting rate, the blog still kept getting regular traffic—particularly Is a Master’s degree in Statistics worthwhile? I guess it’s a burning question these days.
• A big help to my sanity this semester came from joining the All University Orchestra. After a long week of tough classes and research setbacks, it’s great to switch brain modes and play my clarinet. I’ve really missed playing for the past few years in DC, and I’m glad to get back into it.
• Pittsburgh highlights: Bayernhof museum, Pittsburgh Symphony Orchestra concerts (The Legend of Zelda, “Behind the Notes” talks), Jozsa Corner, Point Brugge Cafe, sampling all the Squirrel Hill pizzerias, MCMC Bar Crawl on the Southside Flats, riding the ridiculously steep inclines, Pittsburgh Area Theater Organ Society concerts and tours of their beautiful theater organ
• Things still on our list to do in Pittsburgh: see a CMU theater performance, Pittsburgh aviary and zoo, Kennywood amusement park, Steelers game, Penguins game
• I look forward to getting a chance to teach a whole course this summer. It’ll be 36-309, Experimental Design. I also took some Eberly Center seminars, and the department organized helpful planning meetings for those of us students who’ll teach in the summer, so I feel reasonably prepared.
I plan to have my students design a series of experiments to bake the ultimate chocolate chip cookie. It will be delicious. I baked Meg Hourihan’s mean chocolate chip cookies for a department event earlier this spring, which seems like an appropriate start.
However, ironically, as the local knitr / reproducible research fanboy… I’m supposed to teach the course using SPSS, which seems to be largely point-and-click, without much support for reproducible reports
• It was a nice difference to be on the other side of the department’s open house for admitted students this year I’m also happy to be reading Grad Cafe forums from a much more relaxed point of view this year!
• I’m surprised there’s not much crossover between the CMU and UPitt statistics departments. And the stats community outside each department doesn’t seem as vibrant as it was in DC. I attended the American Statistical Association’s Pittsburgh chapter banquet. Besides CMU and Pitt folks, most attendees seemed to be RAND employees or independent consultants. There are also some Meetup groups: the Pittsburgh Data Visualization Group and the Pittsburgh useR Group.
• I’ve updated and expanded my CMU blogroll in the sidebar. Please let me know if I missed your CMU/Pittsburgh statistics-related blog!

Other people’s helpful posts on the PhD experience: