Living systems are complex. Myriad interacting parts, transient and latent processes, and continuous adaptation to changing conditions occur at nearly every level of biological organization.
Complex biological systems yield complicated data, presenting challenges for data analysis. Often, the assumptions and requirements of standard statistical methods simply don't apply. Yet, until very recently, textbooks and courses on biological statistics have focused almost entirely on standard methods. This has led to misunderstanding and false confidence, and ultimately in some cases, to conflict between the goals and methods of analysis, and improper interpretation of results.
Biologists, faced with difficult data, have turned to Free and Open Source software as an aid to analysis. A virtually unlimited pool of developers gives FOSS the advantage over commercial software -- FOSS support for obscure and specialized methods is unparalleled. R in particular is popular among biologists. But as a programming language, R can be difficult for non-programmers to master. R was built by statisticians, who expected users to be well-versed in both stats and programming. Few biologists have those backgrounds, and learning R has become a common additional challenge to data analysis.
The aim of this course is to help rectify this situation by introducing students to statistical methods better suited to the kinds of analytical problems encountered by biologists, while also imparting essential skills for working with R.
Difficult analytical issues commonly encountered in biology include:
The Applied Biostatistics course will zero-in on recent advances in statistical modeling, while addressing common errors in the application of statistics by biologists. To achieve this goal, the course will build on material typical of introductory statistics courses. For this reason, participation will be limited to students who have completed an introductory statistics course.
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The course is designed to appeal to students in evolutionary biology, molecular biology, and ecology. For the Fall 2016 edition of this course, I intend to cover the following topics. This is a preliminary list that may change.Statistical Topics
To optimize the learning of this technical subject, I intend to provide separate sections for lecture and lab. Generally, the lecture will focus on concepts and the lab will focus on practical skills. However, you will not be sitting passively during lecture. Lecture sessions will include examples in R, which you will be asked to work through during class. This means you will need access to a laptop for both lecture and lab.