Revolution IX: Descriptive statistics, hypothesis testing and quantitative ecology analysis in biological research
Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This course provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity.
The course is addressed to people who were perhaps more confused than enlightened by their lectures in statistics and who have never used a computer for much more than word processing and data entry. From this starting point, it slowly but surely instils an understanding of mathematics, statistics and programming, sufficient for initiating research in ecology. The course’s practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis.
This course does not intend to be a full introduction to statistics. The objective is to review the state-of-the-art statistical methods for analysis of ecological data, demonstrating the power of open source statistical software. We will provide hands-on experience for standard data analysis (cookbook), enabling participants to use the software on their own problems (take-home software).
The focus will be on giving the participants practical experience with R statistical software. The course material will be a blend of introductory lectures on R and practical sessions. Finally, we will walk through quantitative ecology concepts and methodologies, including sampling design, data preparation, diversity analyses, basic ANOVA methods, hypothesis testing, diversity analysis and linear modelling.
This course is divided into 10 theoretical-practical sessions of 4 hours long, including assignments through which you can practice your mastery under supervision. Sessions will take place during the afternoon (14:00 - 18:00 h).
We will provide students with a selection of data sets with which to work, however participants are encouraged to bring their own data.
This course requires some prior experience in statistics and elemental mathematics. Knowing object-oriented programming is not needed.
Call for grants: 30 April
Resolution of grants: April
Start sessions: May