OK, it’s been a busy email day.
From Brandon Nakawaki:
I know your blog is perpetually backlogged by a few months, but I thought I’d forward this to you in case it hadn’t hit your inbox yet. A journal called Basic and Applied Social Psychology is banning null hypothesis significance testing in favor of descriptive statistics. They also express some skepticism of Bayesian approaches, but are not taking any action for or against it at this time (though the editor appears opposed to the use of noninformative priors).
From Joseph Bulbulia:
I wonder what you think about the BASP’s decision to ban “all vestiges of NHSTP (P-values, t-values, F-values, statements about “significant” differences or lack thereof and so on)”?
As a corrective to the current state of affairs in psychology, I’m all for bold moves. And the emphasis on descriptive statistics seems reasonable enough — even if more emphasis could have placed on visualising the data, more warnings could have been issued around the perils of un-modelled data, and more value could have been placed on obtaining quality data (as well as quantity).
My major concern, though, centres on the author’s timidness about Bayesian data analysis. Sure, not every Bayesian analysis deserves to count as a contribution, but nor is it the case that Bayesian methods should be displaced while descriptive methods are given centre stage. We learn by subjecting our beliefs to evidence. Bayesian modelling merely systematises this basic principle, so that adjustments to belief/doubt are explicit.
From Alex Volfovsky:
I just saw this editorial from Basic and Applied Social Psychology: http://www.tandfonline.com/doi/pdf/10.1080/01973533.2015.1012991
Seems to be a somewhat harsh take on the question though gets at the frequently arbitrary choice of “p<.05" being important...
From Jeremy Fox:
Psychology journal bans inferential statistics: As best I can tell, they seem to have decided that all statistical inferences from sample to population are inappropriate.
From Michael Grosskopf:
I thought you might find this interesting if you hadn’t seen it yet. I imagine it is mostly the case of a small journal trying to make a name for itself (I know nothing of the journal offhand), but still is interesting.
From the Reddit comments on a thread that led me to the article:
“They don’t want frequentist approaches because you don’t get a posterior, and they don’t want Bayesian approaches because you don’t actually know the prior.”
From John Transue:
Null Hypothesis Testing BANNED from Psychology Journal: This will be interesting.
From Dominik Papies:
I assume that you are aware of this news, but just in case you haven’t heard, one journal from psychology issued a ban on NHST (see editorial, attached). While I think that this is a bold move that may shake things up nicely, I feel that they may be overshooting, as not the technique per se, but rather its use seems the real problem to me. The editors also state they will put more emphasis on sample size and effect size, which sounds like good news.
From Zach Weller:
One of my fellow graduate students pointed me to this article (posted below) in the Basic and Applied Social Psychology (BASP) journal. The article announces that hypothesis testing is now banned from BASP because the procedure is “invalid”. Unfortunately, this has caused my colleague’s students to lose motivation for learning statistics. . . .
From Amy Cohen:
From the Basic and Applied Social Psychology editorial this month:
The Basic and Applied Social Psychology (BASP) 2014 Editorial emphasized that the null hypothesis significance testing procedure (NHSTP) is invalid, and thus authors would be not required to perform it (Trafimow, 2014). However, to allow authors a grace period, the Editorial stopped short of actually banning the NHSTP. The purpose of the present Editorial is to announce that the grace period is over. From now on, BASP is banning the NHSTP. With the banning of the NHSTP from BASP, what are the implications for authors?
From Daljit Dhadwal:
You may already have seen this, but I thought you could blog about this: the journal “Basic and Applied Social Psychology” is banning most types of inferential statistics (p-values, confidence intervals, etc.).
Here’s the link to the editorial:
John Kruschke blogged about it as well:
The comments on Kruschke’s blog are interesting too.
OK, ok, I’ll take a look. The editorial article in question is by David Trafimow and Michael Marks. Krushke points out this quote from the piece:
The usual problem with Bayesian procedures is that they depend on some sort of Laplacian assumption to generate numbers where none exist. The Laplacian assumption is that when in a state of ignorance, the research should assign an equal probability to each possibility.
Huh? This seems a bit odd to me, given that I just about always work on continuous problems, so that the “possibilities” can’t be counted and it is meaningless to talk about assigning probabilities to each of them. And the bit about “generating numbers where none exist” seems to reflect a misunderstanding of the distinction between a distribution (which reflects uncertainty) and data (which are specific). You don’t want to deterministically impute numbers where the data don’t exist, but it’s ok to assign a distribution to reflect your uncertainty about such numbers. It’s what we always do when we do forecasting; the only thing special about Bayesian analysis is that it applies the principles of forecasting to all unknowns in a problem.
I was amused to see that, when they were looking for an example where Bayesian inference is OK, they used a book by R. A. Fisher!
Trafimow and Marks conclude:
Some might view the NHSTP [null hypothesis significance testing procedure] ban as indicating that it will be easier to publish in BASP [Basic and Applied Social Psychology], or that less rigorous manuscripts will be acceptable. This is not so. On the contrary, we believe that the p < .05 bar is too easy to pass and sometimes serves as an excuse for lower quality research. We hope and anticipate that banning the NHSTP will have the effect of increasing the quality of submitted manuscripts by liberating authors from the stultified structure of NHSTP thinking thereby eliminating an important obstacle to creative thinking.
I’m with them on that. Actually, I think standard errors, p-values, and confidence intervals can be very helpful in research when considered as convenient parts of a data analysis (see chapter 2 of ARM for some examples). Standard errors etc. are helpful in giving a lower bound on uncertainty. The problem comes when they’re considered as the culmination of the analysis, as if “p less than .05” represents some kind of proof of something. I do like the idea of requiring that research claims stand on their own without requiring the (often spurious) support of p-values.