Showing posts from June, 2016

PLOS Computational Biology: Ten Simple Rules for Effective Statistical Practice

A interesting article by six statisticians, Ten Simple Rules for Effective Statistical Practice . Their aim: To this point, Meng notes "sound statistical practices require a bit of science, engineering, and arts, and hence some general guidelines for helping practitioners to develop statistical insights and acumen are in order. No rules, simple or not, can be 100% applicable or foolproof, but that's the very essence that I find this is a useful exercise. It reminds practitioners that good statistical practices require far more than running software or an algorithm." The 10 rules are: Statistical Methods Should Enable Data to Answer Scientific Questions Signals Always Come with Noise Plan Ahead, Really Ahead Worry about Data Quality Statistical Analysis Is More Than a Set of Computations Keep it Simple Provide Assessments of Variability Check Your Assumptions When Possible, Replicate! Make Your Analysis Reproducible