Improving statistical evaluations in the geosciences
Co-organized by AS6/ESSI3/GM12/NH12/SSP5
Thu, 27 Apr, 10:45–12:30 (CEST) Room -2.61/62
Thu, 10:45
A. Fundamental issues in design of experiments and statistical analyses
The following fundamental issues will be addressed:
• Time spent for experimental designs. Advantages and disadvantages of selected experimental designs. Missing randomization. Observational study vs. controlled experiments
• Pseudo-replication vs. true replications and how to deal with it. Wrong model formulations
• “Obsession” with p values: Statistical significance and geoscientific relevance
• Statistical tests: conditions for the application of modelling and hypothesis testing
• Dealing with suspected outliers
• Logistic vs. linear regression
• Number of experimental treatments vs. power of tests. Number of replicates required for predictive modelling
• Use and misuse of correlation analyses
• Investigating and dealing with interactions between factors or predictors
B. Selected advanced issues in geoscientific studies
The following topics will be addressed:
• Validation or cross-validation instead of a sole focus on calibration.
• Model types
• Use of contrasts instead of multiple mean testing
• Different experimental designs – completely randomized (CRD), randomized complete block (RCBD), Latin square (LSD), balanced incomplete bock (BIBD), and split plot design
• RCBD with one treatment factor: analysis of variance and mixed effects model
• Blocked observational study with one predictor: multiple linear regression and mixed effects model
• CRD, RCBD, LSD, split plot design and BIBD: advantages, disadvantages, equations and modelling
• Analysing nested (multi-stratum) designs
Examples will be shown using the programming languages R and SAS