My posting about the statistics profession losing ground to computer science drew many comments, not only here in Mad (Data) Scientist, but also in the co-posting at Revolution Analytics, and in Slashdot. One of the themes in those comments was that Statistics Departments are out of touch and have failed to modernize their curricula. Though I may disagree with the commenters’ definitions of “modern,” I have in fact long felt that there are indeed serious problems in statistics curricula.

I must clarify before continuing that I do NOT advocate that, to paraphrase Shakespeare, “First thing we do, we kill all the theoreticians.” A precise mathematical understanding of the concepts is crucial to good applications. But stat curricula are not realistic.

I’ll use Student t-tests to illustrate. (This is material from my open-source book on probablity and statistics.) The t-test is an exemplar for the curricular ills in three separate senses:

- Significance testing has long been known to be under-informative at best, and highly misleading at worst. Yet it is the core of almost any applied stat course. Why are we still teaching — actually highlighting — a method that is recognized to be harmful?
- We prescribe the use of the t-test in situations in which the sampled population has an exact normal distribution — when we know full well that there is no such animal. All real-life random variables are bounded (as opposed to the infinite-support normal distributions) and discrete (unlike the continuous normal family). [Clarification, added 9/17: I advocate skipping the t-distribution, and going directly to inference based on the Central Limit Theorem. Same for regression. See my book.]
- Going hand-in-hand with the t-test is the sample variance. The classic quantity s
^{2}is an unbiased estimate of the population variance σ^{2}, with s^{2}defined as 1/(n-1) times the sum of squares of our data relative to the sample mean. The concept of unbiasedness does have a place, yes, but in this case there really is no point to dividing by n-1 rather than n. Indeed, even if we do divide by n-1, it is easily shown that the quantity that we actually need, s rather than s^{2}, is a BIASED (downward) estimate of σ. So that n-1 factor is much ado about nothing.

Right from the beginning, then, in the very first course a student takes in statistics, the star of the show, the t-test, has three major problems.

Sadly, the R language largely caters to this old-fashioned, unwarranted thinking. The **var()** and **sd()** functions use that 1/(n-1) factor, for example — a bit of a shock to unwary students who wish to find the variance of a random variable uniformly distributed on, say, 1,2,…,10.

Much more importantly, R’s statistical procedures are centered far too much on significance testing. Take **ks.test()**, for instance; all one can do is a significance test, when it would be nice to be able to obtain a confidence band for the true cdf. Or consider log-linear models: The **loglin()** function is so centered on testing that the user must proactively request parameter estimates, never mind standard errors. (One can get the latter by using **glm()** as a workaround, but one shouldn’t have to do this.)

I loved the suggestion by Frank Harrell in **r-devel** to at least remove the “star system” (asterisks of varying numbers for different p-values) from R output. A Quixotic action on Frank’s part (so of course I chimed in, in support of his point); sadly, no way would such a change be made. To be sure, R in fact is modern in many ways, but there are some problems nevertheless.

In my blog posting cited above, I was especially worried that the stat field is not attracting enough of the “best and brightest” students. Well, any thoughtful student can see the folly of claiming the t-test to be “exact.” And if a sharp student looks closely, he/she will notice the hypocrisy of using the 1/(n-1) factor in estimating variance for comparing two general means, but NOT doing so when comparing two proportions. If unbiasedness is so vital, why not use 1/(n-1) in the proportions case, a skeptical student might ask?

Some years ago, an Israeli statistician, upon hearing me kvetch like this, said I would enjoy a book written by one of his countrymen, titled *What’s Not What in Statistics*. Unfortunately, I’ve never been able to find it. But a good cleanup along those lines of the way statistics is taught is long overdue.