A re-examination of key concepts in statistics: p-values, parameters and their estimates, sampling distributions, hypothesis-testing, effect-size, randomization tests.
Issues in experimental design; namely: Replication, sample size & power-analyses.
How to validate and curate a data-set.
Assumptions of the general-linear model and what to do when the data deviate from them:
- Normality & heterogeneity of variance: generalized-linear-models for binary & count data
- Hierarchical structure in the data: mixed-effects models
How to deal with multiple predictor variables:
- Multiple regression & co-linearity.
- Model-selection (AIC, model-averaging)
Reading:
Zuur, A.F., Ieno, E.N. & Elphick, C.S. (2010) A protocol for data exploration to avoid common statistical problems. Methods in Ecology & Evolution, 1, 3– 14.