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 […]

## UnConMax – uncertainty consideration maxims 7 +/- 2

Warning – this blog post is meant to encourage some loose, fuzzy and possibly distracting thoughts about the practice of statistics in research endeavours. There maybe spelling and grammatical errors as well as a lack of proper sentence structure. It may not be understandable to many or even possibly any readers. But somewhat more seriously, […]

## Should Mister P be allowed/encouraged to reside in counter-factual populations?

Lets say you are repeatedly going to recieve unselected sets of well done RCTs on various say medical treatments. One reasonable assumption with all of these treatments is that they are monotonic – either helpful or harmful for all. The treatment effect will (as always) vary for subgroups in the population – these will not […]

## Zombie student manipulation of symbols/taking of course notes

As with those who manipulate symbols without reflective thought, that Andrew raised, I was recently thinking abouts students who avoid any distraction that might arise by their thinking about what the lecturer is talking about – so that they are sure to get the notes just right. When I was a student I would sometimes […]

## When engineers fail the bridge falls down: When statisticians fail millions of dollars of scarce research funding is squandered and serious public health issues are left far more uncertain than they needed to be

Saw a video link talk at a local hospital based research institute last Friday

Usual stuff about a randomized trail not being properly designed nor analyzed – as if we have not heard about that before

But this time is was tens of millions of dollars and a health concern that likely directly affects over 10% of the readers of this blog – the males over 40 or 50 and those that might care about them

Its was a very large PSA screening study and and

the design and analysis apparently failed to consider the _usual_ and expected lag in a screening effect here (perhaps worth counting the number of statisticians in the supplementary material given)

for an concrete example from colon cancer see here

And apparently a proper reanalysis was initially hampered by the well known – “we would like to give you the data but you know” …. but eventually a reanalysis was able to recover enough of the data from the from published documents

but even with the proper analysis – the public health issue – does PSA screening do more good than harm ( half of US currently males get PSA screening at some time? ) will likely remain largely uncertain or at least more uncertain than it needed to be

and it will happen again and again (seriously wasteful and harmful design and analysis)

and there will be a lot more needless deaths from either “screening being adopted” if it truly shouldn’t have been or “screening was not more fully adopted, earlier” when it truly should have been (there can be very nasty downsides from ineffective screening programs, including increased mortality)

## Statistics is easy! part 2.F making it look easy was easy with subtraction rather than addition

After pointing out that getting a true picture of how log prior and log likelihood add to get the log posterior – was equivalent to getting a fail safe diagnostic for MCMC convergence

I started to think that was bit hard – to just get a display to show stats was easy …

But then why not just subtract?

## Statistics is easy! part 2.1 – can we avoid unexpected bumps when making it look easy?

I increased the range of the plot from Statistics is easy! part 2 and added the 2.5% and 97.5% percentiles from a WinBugs run on the same problem … using bugs() of course

And then started to worry about that nasty bump on the right of the 97.5% percentiles

## Statistics is easy! part 2 – can we at least make it look easy?

Well can we at least make it look easy?

For the model as given here, there are two parameters Pc and Pt – but the focus of interest will be on some parameter representing a treatment effect

– Andrew chose Pt – Pc.

But sticking for a while with Pt and Pc – the prior is a surface over Pt and Pc as is the data model (likelihood)

In particular, the prior is a flat surface (independent uniforms)

and the likelihood is Pt^1 (1 – Pt)^29 * Pc^3 (1 – Pc)^7 (the * is from independence)

(If I reversed the treatment and control groups – I should be blinded to that anyways)