Category: NHST

Replications: How Should We Analyze the Results?

Does This Effect Replicate? It seems almost irresistible to think in terms of such a dichotomous question! We seem to crave an ‘it-did’ or ‘it-didn’t’ answer! However, rarely if ever is a bald yes-no decision the most informative way to

eNeuro’s new push to encourage estimation

eNeuro, one of the two journals published by the Society for Neuroscience, has revised its author guidelines to encourage estimation. That’s great news. Here is: The announcement and comment from editor Christophe Bernard An accompanying commentary from us that explains

To Understand (or Teach) CIs, Adopt an Estimation Mindset

Update 8 June. Some minor tweaks. Addition of the full reference for two papers mentioned. Of course I would say that, wouldn’t I?! It’s the basis of ITNS and a new-statistics approach. But the latest issue of SERJ adds a

Do People Have a Binary Bias?

For years I’ve been working on changing my thinking–even when just musing about nothing in particular–from “I wonder whether…” to “I wonder to what extent…”. It has taken a while, but now I usually do find myself thinking in terms

The TAS Articles: Geoff’s Take

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 Beyond p < .05: The Latest

A couple of days ago, the three authors of the Nature paper accompanying the special issue of TAS on moving beyond p < .05 sent the update below. (See below for lots of links.) We are writing with a brief

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:

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

Top