Learn to design studies, manage data and use statistics effectively. If you have the opportunity, it beats hiring someone to do it! My focus is on recognizing and choosing appropriate methods to obtain a meaningful result.


Workshops are hands on and ideal for small groups to learn a set of skills. Participants typically get practice with a basic set of skills (e.g. analyzing count data) and are given the tools to take it further.

Seminars or short lectures introduce a skill to small and large groups. Participants learn the basics of a skill so they can understand the reasoning and recognize when to apply it (e.g. survival analysis).

Private consultations are best when you need to master a new skill. This format combines theory and practice, with the possibility of incorporating your own study or data set.


Designing a study or experiment. Some topics to be covered:

  • keep your objective in mind (e.g. test a hypothesis, make a prediction, evaluate several models).
  • avoid pitfalls or biases (e.g. pseudoreplication, confounding variables, sampling bias, etc.)
  • statistical power, effect size, sample size, and statistical methods for your study design.

Managing and visualizing data:

  • recording and extracting data effectively, including implicit data
  • exporting, preparing, and manipulating data
  • summarizing and checking data using descriptive statistics and figures
  • visualization of data structure, comparisons and relationships

Statistical analyses

  • choosing statistical methods according to your data and objectives
  • verification of assumptions and diagnostics
  • standard parametric tests: t-tests, ANOVAs, Pearson’s correlation, least square regression, general linear models
  • standard nonparametric tests: Mann Whitney-U, Spearman correlation, and rank based tests
  • robust/resampling statistics: bootstrap, jackknife, permutations
  • nonlinear models: curve fitting, quadratic, exponential, logistic…
  • generalized linear models: Normal, Poisson, Binomial, Gamma and other distributions,
  • mixed models: fixed factor, random factor, correlation structure
  • survival analyses: Cox proportional hazard, time-dependent variables
  • phylogenetic regression: sequence alignment, tree building
  • model selection: information criteria, AIC, BIC, QIC, model averaging
  • introduction to R: R language for statistics and graphics (open source), data input/output and manipulation, basic plots, statistics and programming.



Both the format and the content will be customized to your needs. I also adapt my teaching methods to the material and to your learning style. Please contact me to determine the best way for you to meet your learning objectives.