I was asked to prepare a proposal on a short course about consulting for an upcoming Statistics conference. I had talked about this in an earlier blog post. Here is the official submission which includes the course description, outline and objectives, some information about my qualifications to teach the class, and a summary of how this class fits into the theme of the conference. Here’s what I wrote. Continue reading
Author Archives: pmean
Recommended: A review of methods for missing data
This is a nice summary of the advantages and disadvantages of various methods for handling missing values. Continue reading
Recommended: Meet John Gawalt, Director of the National Center for Science and Engineering Statistics
This is a brief interview with John Gawalt, the new director of the National Center for Science and Engineering Statistics within the National Science Foundation. Continue reading
PMean: Using BUGS within the R programming environment
I am giving a talk today for the Kansas City R Users group about BUGS (Bayes Using Gibbs Sampler). I have already written extensively about BUGS and the interface to BUGS from within the R programming environment, and you can find these on my category page for Bayesian statistics. Here is a quick overview of why you might want to use BUGS and how you would use it. I’ve included links to the relevant pages on my website so you can explore this topic further on your own. Continue reading
Recommended: 8 tips for doing data visualization right.
This slide show includes some examples of really bad (and a few really good) graphics with some explanations of general principles for data display. Continue reading
PMean: Proposed outline for “How to Start and Run an Independent Statistical Consulting Business”
There has been a bit of discussion on submitting a proposal to teach a class on running an independent consulting business for an upcoming statistics conference. I think it would be a great idea and I want to suggest a tentative outline. Continue reading
Recommended: Data doesn’t speak for itself
This blog post explains that you can’t just put a graph up on a screen and immediately expect people to understand it. You need to provide critical context to help your audience. Continue reading
Recommended: Not all scientific studies are created equal
This video gives a non-technical overview of the strengths and weaknesses of observational studies. Continue reading
Pmean: The IRB questions my sample size calculation
I got a question today from someone submitting a research protocol to the Institutional Review Board (IRB). The IRB had some concerns about the power calculation. In particular, they said “The IRB would like to know, how you set the parameters for the power calculation, such as effect size, alpha level. For effect size, you need to have some data to justify or should choose a conservative one.”
Part of this was due to an error in the submission of the protocol. We had specified a paired t-test rather than an independent samples t-test, which is a major gaffe on my part. But they were pushing into some tricky territory and I wanted to clear things up. Here is the response that I suggested that we share with the IRB. Continue reading
Recommended: The random risks of randomised trials
This is an overview of some of the ethical controversies associated with randomization. It includes an interesting story of an early trial by Archie Cochrane that raised a lot of fuss at the time because it was attacking one of the “sacred cows” of medicine. Continue reading