Tagged: R

Mathematical Biostatistics Boot Camp on Coursera

mathbiostatCoursera is hosting a Mathematical Biostatistics Boot Camp by John Hopkins School of Public Health. Here are some excerpts from the course web page:


This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.

After completing this course, students will have a basic level of understanding of the goals, assumptions, benefits and negatives of probability modeling in the medical sciences. This understanding will be invaluable when approaching new statistical topics and will provide students with a framework and foundation for future self learning. Topics include probability, random variables, distributions, expectations, variances, independence, conditional probabilities, likelihood and some basic inferences based on confidence intervals.


7 weeks, starting 16th April 2013. Workload of 3-5 hrs per week.


In his charming and entertaining way, Dr. Jeremy Fox very nonchalantly ends his ‘Advice : tips on stats’(in the ‘Advice’ series of posts in Dynamic Ecology) – “Don’t just blindly follow the “rules”. Rules are not a substitute for thought.” Having given up Math as a subject after 12th standard, confronting statistics suddenly and out of the blue at the PhD level was quite a rude shock for me. To make matters worse, most of my classmates seemed to know the fundamentals of stats, why one did statistical tests and what statistical software to use. I unfortunately also attended a statistics course (ah, those credits are a bane) in a non-Biological Sciences department and (no prizes for guessing) got thoroughly overwhelmed. But the worst was yet to come – my own data were to give me the most vivid nightmares of them all (I have been grappling with very naughty data sets for a very long time without being able to convince reviewers that they are in fact very well behaved).

As a naive user of statisconfusedtics, I wonder if it is as simple to ‘not blindly follow rules’. I intend to use statistics merely as a tool to state with conviction that what I observe in nature is reasonably true (hopefully at p < 0.05, or 0.1 whenever ‘ecologically relevant’; but let’s put that discussion off for another time). And unfortunately, the only way I can ensure that I have done the statistics bit correctly, is if I have followed the rules. For the inexperienced, a set of rules to follow is often the only way forward in the confusing maze of multiple ways of achieving the same thing.

Dr. Fox uses subtle metaphors (in a way only experienced scientists can) to make the task of doing good stats sound less daunting – “Statistics is like cooking—there are recipes to follow, but not all of them are good ones, and even the good ones are treated more like guidelines by the best cooks, who can judge when, why, and how to deviate from the recipe”. How does one become a ‘good cook’? I guess the best cooks start off following the rules before they put creativity and thought into the process. When a subject is alien and unintuitive, is it really that simple to start with a ‘thought’ than with a tried and tested ‘rule’? I was wondering if everyone feels the same way or if I am the only one here who completely missed the boat? (I exclude the physicist and the mathematician amongst us from this discussion; their practical advice would be to stop doing statistics altogether. I second that motion.)