This course will encourage students not take empirical or statistical claims for granted and it will equip them with the skills to critically evaluate these claims. This will be accomplished (almost completely) without mathematics – just with analytical and logical thinking.
Often, we are confronted with seemingly clear empirical facts that we interpret as obvious causal relations between two or more empirical factors. For example, an increase in revenues of a firm after the change of the CEO will usually be causally related to the CEO-change. Is this really the case? Or do we have to look for different causes of these relations?
There exist many statistical pitfalls and fallacies that we tend to overlook, or are even unaware of, in everyday life. This course introduces students to these pitfalls and enables them to read statistical arguments critically. Covering key areas of where statistical claims can ‘go wrong’ – that is, where false conclusions tend to be drawn from seemingly self-evident data – the course sharpens students’ analytical capabilities, allowing them to make better decisions in their professional career.
The course consists of seminars. It is a practice-based course, where students participate in experiments or solve in-class exercises either individually or in small groups.
The grade is based on the final assignment. The final assignment is an exam on the topics discussed in class.
Textbooks and/or bibliography
Selected chapters of
Campbell, S. K. (2012). Flaws and fallacies in statistical thinking. Englewood Cliffs: Prentice-Hall
Clark, M. (2007). Paradoxes from A to Z. 2nd ed. London: Routledge