Modern statistics in natural sciences, 5 credits
Modern statistik i naturvetenskaperna
Course information
Language of instruction: English
Course period: January-March 2024.
Schedule: http://arnqvist.org/stat/schedule_VT2024.pdf
Course structure: On campus. Lectures, practical problem solving, group discussions, reading of course book.
Recommended prerequisites
Basic statistical knowledge.
Learning outcomes
The course develops and discusses a number of fundamental and general goals in postgraduate education, such as scientific inferences, limitations of dualism, hypothesis testing, scientific transparency, good statistical practice, experimental design and numerical analysis. More specific goals include insights into choice, and execution, of statistical models and interpretation of statistical analysis, which is key in all fields leaning on empirical data. In summary, the course significantly improves the participants’ knowledge and understanding of scientific methodology, statistical analysis and critical evaluation of scientific inferences. These are all central goals in post-graduate training.
Learning outcomes for doctoral degree
The course develops and discusses a number of fundamental and general goals in postgraduate education, such as scientific inferences, limitations of dualism, hypothesis testing, scientific transparency, good statistical practice, experimental design and numerical analysis. More specific goals include insights into choice, and execution, of statistical models and interpretation of statistical analysis, which is key in many fields.
Course contents
The course is focused on analyses of experimental data, but observational data analysis is also covered briefly. The course focusses on linear models and includes: experimental designs leading to ANOVA or ANCOVA, mixed models, blocked experiments, repeated measurement designs, nested and factorial designs, multiple regression including strategies for selecting variables and evaluating models, generalized linear models (GLIM) including logistic and Poisson regression, contingency table tests, power analysis, multivariate analysis and ordination techniques, resampling and permutation statistics, Bayesian model fitting, MCMC techniques, geometric morphometrics and a few other topics. The philosophical basis of hypothesis-testing and statistical inferences is covered at the start of the course and the course closes with considering good scientific practice in terms of analyses.
Instruction
The core of the course is built around a series of 13 half-day and interactive lectures. In addition, the students then work off-schedule with a series of common practical elements/problems that are then discussed during a series of tutored group discussions. The participants deliver written practical reports on these exercises. Hands-on advice and individual tutoring of the use of statistical software (R) is also offered at several occasions during the course. The course closes with a group discussion of a series of statistical problems.
Assessment
Attendance at all lectures and approved individual practical reports that students hand in.
Course examiner
Göran Arnqvist, Goran.Arnqvist@ebc.uu.se
Department with main responsibility
Department of Ecology and Genetics
Contact person
Göran Arnqvist, Goran.Arnqvist@ebc.uu.se
Application
Submit the application for admission to: https://forms.gle/8PKR5Et8JZrVPXLW9
Submit the application not later than: December 31, 2023