Inference and Models

Statistical inference is intended to aid in answering scientific questions about a population, based on a sample from this population, i.e. on data that are subject to variability. The data generating mechanism is described as a probability model that is completely specified except for a limited number of unknown parameters. The questions that can be answered are a) are the data consistent with the model? and b) assuming that a) is fulfilled, what can be concluded about values of the unknown parameters? In this course, the basic principles of statistical inference are presented, with an emphasis on likelihood methods. Methods are illustrated by the classical linear model.

At the end of the course, the student will:

• understand the sampling principles of statistical inference
• be familiar with the principles of likelihood theory
• know the different types of hypothesis tests
• know the standard methods of point estimation
• know the standard methods of interval estimation
• be familiar with numerical methods for statistical inference
• know different modeling strategies and when to use them

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

1 week, fulltime (face to face)

Please note that this course is part of an existing program within the Graduate School of Life Sciences.