Improving statistical evaluations in the geosciences



This short course aims to address potential problems in geoscientific studies and to reduce the number of non-reproducible studies.

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 additional issues in geoscientific studies
In some studies, improvements may be possible and the following fields will be addressed.
• Dealing with variance heterogeneity
• Use of contrasts instead of multiple mean testing
• Use of mixed regression and anova models
• Including squared and cubic contributions in models instead of solely relying on linear contributions. Lack of fit
• Box Cox transformation
• Validation or cross-validation instead of a sole focus on calibration.
• Model types

Examples will be shown using the software R

Convener: Bernard Ludwig | Co-convener: Anna Gunina
Fri, 27 May, 08:30–10:00 (CEST)
Room -2.85/86


  • Bernard Ludwig, University of Kassel, Germany