Category: The New Statistics

Ditching Statistical Significance: The Most Talked-About Paper Ever?

Well, that might be a stretch, but in relation to the Nature Comment that Bob and I signed to support, Altmetric tweeted: John Ioannidis published this criticism of the Comment, with the subtitle Do Not Abandon Significance. Much of what

Moving to a World Beyond “p < 0.05”

The 43 articles in The American Statistician discussing what researchers should do in a “post p<.05” world are now online. See here for a list of them all, with links to each article. The collection starts with an editorial: Go

Ditching Statistical Significance?!

Nature (!) has just published an editorial discussing and advocating that statistical significance should be ditched. For me, that’s the stuff of dreams, but I have lived to see it happen! I’m so happy! Here’s one para from the editorial:


Last month I (Bob) visited a local elementary school for a “Science Alliance” visit. This is a program in our community to being local scientists into the classroom. I brought the Cartoon Network simulator I have been developing (Calin-Jageman, 2017,

Journal Articles Without p Values

Once we have a CI, a p value adds nothing, and is likely to mislead and to tempt the writer or readers to fall back into mere dichotomous decision making (boo!). So let’s simply use estimation and never report p

Teaching The New Statistics: The Action’s in D.C.

The Academy Awards are out of the way, so we can focus on what’s really important: the APS Convention, May 23-26, 2019, in Washington D.C. For the first time for many years I won’t be there, but new-statistics action continues

A Second Edition of ITNS? Here’s the Latest

Our first blog post about a possible second edition of ITNS is here. All the comments I made there, and the questions I asked, remain relevant. We’ve had some very useful feedback and suggestions, but we’d love more. You could

Sadly, Dichotomous Thinking Persists in HCI Research

A few words about the latest from Pierre Dragicevic. He’s an HCI researcher in Paris who totally gets the need for the new statistics. I’ve written about his work before, here and here. Now, with colleague Lonni Besançon, he reports

Internal Meta-Analysis: The Latest

I recently wrote in favour of internal meta-analysis, which refers to m-a that integrates evidence from two or more studies on more-or-less the same question, all coming from the same lab and perhaps reported in a single article. The post

Abandon Statistical Significance!

That’s the title of a paper accepted for publication in The American Statistician. (I confess that I added the “!”) The paper is here. Scroll down below to see the abstract. The paper boasts an interdisciplinary team of authors, including