Category: Stats tools

Microworlds

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,

The Multiverse! Dances, and More, From Pierre in Paris

Our Open Science superego tells us that we must preregister our data analysis plan, follow that plan exactly, then emphasise just those results as most believable. Death to cherry-picking! Yay! The Multiverse But one of the advantages of open data

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

Open Science DownUnder — Fiona Fidler reports

Last week, the 2018 Australasian Open Science Conference was held in Brisbane at the University of Queensland: The first conference in Oz on the themes of Open Science and how to improve how science is done. They expected 40 and

Precision for Planning: Great New Developments

–updated with a link from Ken Kelley to access the functions in the paper, 6/28/2018– In a new-statistics world, the best way to choose N for a study is to use precision for planning (PfP), also known as accuracy in

APS in San Fran 3: Workshop on Teaching the New Stats

Tamarah Smith and Bob presented a workshop on Teaching the New Stats to an almost sold-out crowd. I wasn’t there, but by all reports it went extremely well. Such a workshop seems to me a terrific way to help interested

Tony Hak 1950-2018: Champion of Better Methods, Better Statistics

It was a shock to receive the very sad news that Tony Hak died last week, unexpectedly. Too young! And only 3 years into an active retirement. Tony was an Emeritus Associate Professor, having retired in 2015 from the Department

Measuring Heterogeneity in Meta-Analysis: The Diamond Ratio (DR)

This is a post about the Diamond Ratio (DR), a simple measure of the extent of heterogeneity in a meta-analysis. We introduced the DR in ITNS. But first, some background. Fixed Effect (FE) model for meta-analysis The diamond at the

Why effect sizes? A tutorial (especially for Neuroscientists)

The New Statistics emphasizes effect sizes, confidence intervals, meta-analysis, and Open Science.  There’s a lot of momentum to adopt this change of focus.  For example, the APA recently released new guidelines for reporting quantitative research and throughout it emphasizes reporting

Some Questions–Would You Care to Comment?

Blogs sometimes either elicit lots of comments, or they don’t. While writing, I’m always wondering how readers might react, what they (you) might be thinking. In my most recent post I asked about experiences or thoughts readers might have about

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