Category Archives: Statistics

Recommended: How to be more effective in your professional life

This article starts with a nice anecdote about being dismissive about what someone else is saying ends up hurting you. It also provides a nice structure, POWER, for organizing consulting meetings. POWER stands for Prepare, Open, Work, End, and Reflect. This article was a basis for some of the content in an interesting webinar on consulting. Continue reading

PMean: What are we doing to justify all that time we’re budgeting?

An email discussion about the appropriate percentage effort on research grants has produced a lot of interesting discussions. One person raised an interesting question. The typical data analysis, he claimed, might involve a few hours reviewing the input data set, a few hours conducting the analysis and a few hours preparing a statistical summary, but even after a generous estimate of the work at each of the time points, he could only come up with 22 hours of effort, which corresponds roughly with a 1% FTE. I wrote back describing some of the things that might occur before the data analysis that might add time to this effort. Continue reading

PMean: Draft policy on statistical support for research

I am drafting up a policy on statistical support for research at my part-time job at UMKC. It is loosely based on standards at the University of California, Davis and Kansas University Medical Center. An early draft appears below. I’ve gotten some suggestions that setting a minimum percentage effort is a bad idea. What do you think?

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Recommended: Definitions of Criteria and Considerations for Research Project Grant Critiques

I have to help write NIH grants from time to time, and I need to always keep front and center the criteria that NIH peer reviewers use when they evaluate grants. They look at five broad areas: significance, investigators, innovation, approach, and environment. This document explains what each of these five broad areas means.  Continue reading