[visionlist] Statistical Criticism is Easy; I Need to RememberThat Real People are Involved

[Roland Fleming]

Hi Todd,

I’m pretty sure that’s why they are advocating Bayesian approaches that (supposedly) do allow you to evaluate the evidence for the null hypothesis, as in:

Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic bulletin & review, 16(2), 225-237.

— R


> On 16 Nov 2017, at 21:45, Horowitz, Todd (NIH/NCI) [E] <todd.horowitz at nih.gov> wrote:
> 
> Oops!
>  
> What I meant to say was that a p-value tells you how likely your data are given the null hypothesis, it doesn’t really say anything about the probability of the null hypothesis. So a SMALL p-value means that my data are unlikely given the null hypothesis, and a LARGE p-value means my data are likely given the null-hypothesis... but they could be even more compatible with some other hypothesis!
>  
> thanks
> Todd
>  
> From: Horowitz, Todd (NIH/NCI) [E] [mailto:todd.horowitz at nih.gov] 
> Sent: Thursday, November 16, 2017 2:00 PM
> To: Pam Pallett <ppallett at gmail.com>; visionlist at visionscience.com
> Subject: Re: [visionlist] Statistical Criticism is Easy; I Need to Remember That Real People are Involved
>  
> I thought that a a large p-value simply meant that my data were unlikely given the null-hypothesis, a statement which yields no evidence about either the null- or alternative hypotheses.
>  
> From: Pam Pallett [mailto:ppallett at gmail.com] 
> Sent: Thursday, November 16, 2017 10:29 AM
> To: visionlist at visionscience.com
> Subject: [visionlist] Statistical Criticism is Easy; I Need to Remember That Real People are Involved
>  
> Hi All,
>  
> I came across a blog today by Frank Harrell, Professor of Biostatistics and Founding Chair at Vanderbilt.  His most recent post is the title of this email.  But as I'm reading through his blog, I'm hearing a lot that has been discussed and experienced by professors and postdocs subscribed to this list.  We are often very separated from our neighboring departments, and I actually found some comfort in the fact that these problems seem spread across the board (misery loves company). Even if we have been echoing these problems for over a decade with little effective change.  
>  
> In his most recent post he says, "There are several ways to improve the system that I believe would foster clinical research and make peer review more objective and productive." I'm curious about what the people in the vision community think of these suggestions and whether they are realistic to implement in our field.  His list is at the bottom of the entry.  http://www.fharrell.com/2017/11/
>  
> For those experiencing TL;DR, here is the shortlist:
> ·         Have journals conduct reviews of background and methods without knowledge of results.
> ·         Abandon journals and use researcher-led online systems that invite open post-"publication" peer review and give researchers the opportunities to improve their "paper" in an ongoing fashion.
> ·         If not publishing the entire paper online, deposit the background and methods sections for open pre-journal submission review.
> ·         Abandon null hypothesis testing and p-values. Before that, always keep in mind that a large p-value means nothing more than "we don't yet have evidence against the null hypothesis", and emphasize confidence limits.
> ·         Embrace Bayesian methods that provide safer and more actionable evidence, including measures that quantify clinical significance. And if one is trying to amass evidence that the effects of two treatments are similar, compute the direct probability of similarity using a Bayesian model.
> ·         Improve statistical education of researchers, referees, and journal editors, and strengthen statistical review for journals.
> ·         Until everyone understands the most important statistical concepts, better educate researchers and peer reviewers on statistical problems to avoid.
>  
> Best,
> Pam Pallett
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