Recently I was reminiscing with an old colleague about how our publications from almost 30 years ago that tried to encourage better conduct and reporting of clinical research seemed to have had so little impact. This one for instance. Recently, they suggested there is some reason to hope for better, pointing to a website reporting […]

## What I missed on fixed effects (plural).

In my [Keith] previous post that criticised a publish paper, the first author commented they wanted some time to respond and I agreed. I also suggested that if the response came in after most readers have moved on I would re-post their response as a new post pointing back to the previous. So here we are. […]

## What am I missing and what will this paper likely lead researchers to think and do?

This post is by Keith. In a previous post Ken Rice brought our attention to a recent paper he had published with Julian Higgins and Thomas Lumley (RHL). After I obtained access and read the paper, I made some critical comments regarding RHL which ended with “Or maybe I missed something.” This post will try to discern […]

## What you value should set out how you act and that how you represent what to possibly act upon: Aesthetics -> Ethics -> Logic.

I often include references to CS Peirce in my comments. Some might think way too often. However, this whole post will be trying to extract some morsels of insight from some of his later work. With the hope that it will enable applying statistics more thoughtfully. Now, making sense of Peirce, that is getting him […]

## What to make of reported statistical analysis summaries: Hear no distinction, see no ensembles, speak of no non-random error.

Recently there has been a lot of fuss about the inappropriate interpretations and uses of p-values, significance tests, Bayes factors, confidence intervals, credible intervals and almost anything anyone has ever thought of. That is to desperately discern what to make of reported statistical analysis summaries of individual studies – largely on their own. Including a credible […]

## Seemingly intuitive and low math intros to Bayes never seem to deliver as hoped: Why?

This post was prompted by recent nicely done videos by Rasmus Baath that provide an intuitive and low math introduction to Bayesian material. Now, I do not know that these have delivered less than he hoped for. Nor I have asked him. However, given similar material I and others have tried out in the past that […]

## Take two on Laura Arnold’s TEDx talk.

This post is by Keith. In this post I try to be more concise and direct about what I found of value in Laura Arnold’s TEDx talk that I recently blogged about here. Primarily it was the disclosure from someone who could afford to buy good evidence (and experts to assess it) that they did not think good […]

## Representists versus Propertyists: RabbitDucks – being good for what?

It is not that unusual in statistics to get the same statistical output (uncertainty interval, estimate, tail probability,etc.) for every sample, or some samples or the same distribution of outputs or the same expectations of outputs or just close enough expectations of outputs. Then, I would argue one has a variation on a DuckRabbit. In […]

## Applying statistics in science will likely remain unreasonably difficult in my life time: but I have no intention of changing careers.

This post is by Keith. image (Image from deviantart.com) There are a couple posts I have been struggling to put together, one is on what science is or should be (drawing on Charles Peirce). The other is on why a posterior is not a posterior is not a posterior: even if mathematically equivalent – they […]

## The Prior: Fully comprehended last, put first, checked the least?

Priors are important in Bayesian inference. Some would even say : ” In Bayesian inference you can—OK, you must—assign a prior distribution representing the set of values the coefficient [i.e any unknown parameter] can be.” Although priors are put first in most expositions, my sense is that in most applications they are seldom considered first, are […]

## Missed Friday the 13th Zombie Plot Update

The revised paper plot13.pdf Slightly improved figures figure13.pdf And just the history part from my thesis – that some find interesting. (And to provide a selfish wiki meta-analysis entry pointer) JustHistory.pdf I have had about a dozen friends read this or earlier versions – they split into finding it interesting (and pragmatic) versus incomprehensible. The […]

## Explaining that plot.

With some upgrades from a previous post. And with a hopefully clear 40+ page draft paper (see page 16). Drawing Inference – Literally and by Individual Contribution.pdf Comments are welcome, though my reponses may be delayed. (Working on how to best render the graphs.) K? p.s. Plot was modified so that it might be better […]

## What Zombies see in Scatterplots

This video caught my interest – news video clip (from this post2) http://www.stat.columbia.edu/~cook/movabletype/archives/2011/02/on_summarizing.html The news commentator did seem to be trying to point out what a couple of states had to say about the claimed relationship – almost on their own. Some methods have been worked out for zombies to do just this! So I […]

## Attractive models (and data) wanted for statistical art show.

I have agreed to do a local art exhibition in February. An excuse to think about form, colour and style for plotting almost individual observation likelihoods – while invoking the artists privilege of refusing to give interpretations of their own work. In order to make it possibly less dry I’ll try to use intuitive suggestive […]

## Biostatistics via Pragmatic and Perceptive Bayes.

This conference touches nicely on many of the more Biostatistics related topics that have come up on this blog from a pragmatic and perceptive Bayesian perspective. Fourth Annual Bayesian Biostatistics Conference Including the star of that recent Cochrane TV debate who will be the key note speaker. See here Subtle statistical issues to be debated […]

## Course proposal: Bayesian and advanced likelihood statistical methods for zombies.

The course outline ZombieCourseOutline.rtf Hints/draft R code for implementing this for a regression example from D. Pena x=c(1:10,17,17,17) y=c(1:10,25,25,25) ZombieAssign1.txt The assignment being to provide a legend that explains all the lines and symbols in this plot ZombieAssign1.pdf With a bonus assignment being to provide better R code and or techniques. And a possible graduate […]