This course aims at bringing students into a position in which they will not take every empirical or statistical result for granted. This will be accomplished (almost completely) without mathematics – just with analytical and logical thinking. Moreover, I will also cover in this course some important topics related to the writing of a Master Thesis.
Oftentimes we are confronted with seemingly clear empirical facts that we interpret as obvious causal relations between two or (very rarely) more empirical factors. For example: an increase in revenues of a firm after the change of a CEO will usually be causally related to the CEO-change. The higher rejection rate for women who apply for admission at a university, on the other hand, will usually be interpreted as a discriminatory act of the university. But: is this really the case? Or do we have to look for different causes of these relations? Indeed, there exist many statistical pitfalls and fallacies that we usually tend to overlook, or are even unaware of, in everyday life. Very often these pitfalls exist because we do not take the influence of randomness into account. This course wants to uncover these pitfalls and fallacies and help future managers, not to get misled by seemingly obvious results. This course fully aims at sharpening the analytical capabilities of students which should help them making better decisions in their professional life with the help of several experiments in which the students participate. We will also discuss, the different aspects of an empirical study that need to be taken into account when writing a Master thesis.