Statistical errors are all too common in medical literature, and contribute to the reproducibility crisis currently plaguing science. Fortunately, you don’t need a degree in statistics to catch these errors. While some errors are impossible to spot without access to the underlying dataset, many are detectable just by reviewing the information available in the paper. In many cases nothing more than common sense and simple arithmetic is required. In addition, there is an ever-increasing number of free, easy-to-use online statistical tools that facilitate error detection.
In this session, Dr. Sainani will demonstrate how to apply these statistical sleuthing tools using real examples from medical literature. She will also review principles of effective statistics that can help prevent statistical errors from occurring in the first place.
You will learn:
- Simple ways to detect statistical errors in medical literature
- How to avoid errors by using principles of effective statistics
- How to use free, online statistics tools that don’t require a programming background
About the Speaker
Kristin Sainani (née Cobb) is an associate professor at Stanford University. She teaches statistics and writing; works on statistical projects in sports medicine; and writes about health, science and statistics for a range of audiences. She authored the health column Body News for Allure magazine for a decade. She is also the statistical editor for the journal Physical Medicine & Rehabilitation; and she authors a statistics column, Statistically Speaking, for this journal. She teaches the popular Massive Open Online Course (MOOC) Writing in the Sciences on Coursera. She was the recipient of the 2018 Biosciences Award for Excellence in Graduate Teaching at Stanford University.