Statistical Analyses and Consulting
Make the most of your data and convince with a robust statistical analysis!
If you are planning a study or when you are analysing your data statistical analyses are always the key for determining the result of a study. However, showing that the correct analyses have been made is equally impotant for risk managers.
We provide solid statistical evaluations and help to communicate the results in an easily understandable way. For R&D projects, we help to get the most of your data using various exploratory techniques.
- Evaluation of laboratory and field studies (consumer safety/toxicology, ecotoxicology, enviromental fate)
- Evaluation of monitoring studies
- Statistical consulting (e.g. study design)
- Hypothesis testing (parametric and non-parametric tests, such as Mann-Whitney-U ,Wilcoxon, ANOVA, Student-t, Post-Hoc tests, etc.)
- Uni- and multivariate statistics (field studies, mesocosm studies; evaluation according to RIVM guidance by De Jong et al. 2010)
- Principle response curves (PRC, Van den Brink and Ter Braak, 1999)
- Power analysis (parametric), Monte Carlo power analysis
- Development of automated statistical analyses for routine tasks
- Development of tailor-made software tools for special statistical analyses
We help to find the best study design for your cost intensive studies, making sure that you get the most out of your study. A question might be if it is better to increase the number of replicates or sample size? We help to find the most efficient study design and sampling scheme, and to avoid pseudoreplication (artificial increase of sample size). Power analysis can additionally help to determine either the sample sizes needed to answer a specific question, or it can be applied to support your results.
Crocker J. & Wang M. 2008. How to estimate PD – Appendix 29. In: Scientific opinion of the panel on plant protection products and their residues on a request from the EFSA (European Food Safety Authority) PRAPeR Unit on risk assessment for birds and mammals. The EFSA Journal 734: 1-181.