At the end of this course, you are able to:

• translate univariate problems from scientific practice to statistically meaningful questions and solutions
• perform the following statistical analyses in a correct way, using univariate data:
  • parametric hypothesis test
  • non-parametric hypothesis test
  • linear regression
  • Analysis of Variance (ANOVA)
• construct a simple experimental design to evaluate the influence of multiple factors on the outcome of an experiment
• interpret the output from statistical software, given as p-values or figures

Repeated measurements often do not lead to identical results due to the occurrence of random errors, already discussed during Chemical Analysis (NWI-MOL001). Therefore, a basic knowledge of statistics is indispensable for every student in the molecular sciences. Statistics may for instance be used to draw statistically founded conclusions regarding the influence of experimental conditions (for example "A higher temperature leads to a significantly higher yield"). Also, statistics provides ways to set up a set of experiments such that the most information is obtained with the least amount of effort ('Design of experiments').

When performing experiments and gathering results yourself, as you will do frequently during your studies, it is of the utmost importance to correctly interpret these results. During this course, you will learn to draw conclusions based on the outcome of statistical tests. Most of these are based on univariate data: data where for each object/sample 1 property has been measured. Statistical analyses for data with more than 1 property per object/sample will be discussed during the lectures. We will also take a look at different experimental designs.

The course consists of lectures and workshops. Each week starts with a short workshop in which you get acquainted with the theory using multiple-choice questions. You are expected to have studied the reader prior to this workshop; during the workshop there is not much time to read and study the reader. During the lecture, which follows the introductory workshop, some aspects of that week’s subject will be explained in more detail or deepened. Moreover, extensions to multivariate statistics will be provided. In the second workshop, you will perform statistical analyses yourself and draw conclusions based their outcome.