Key Concepts of Modern Statistical Thinking

This course equips students with understanding of the key statistical and non-statistical concepts that are required for development of modern statistical thinking - the thinking that enables us to analyse data in a scientifically objective way.

Students learn about the science behind causal thinking, which enables us to become familiar with influential non-statistical biases that are regularly transmitted to statistical biases, making data-insights prone to bias of unknown dimensions. Furthermore, students learn about omnipotence of statistical assumptions and their impact on credibility of data-insights. Students also learn about ways to assess trustworthiness of presented data-insights.

In order for data to provide trustworthy data-insights, modern statistical science emphasises the importance of a carefully planned and executed study design. During this course students learn what the study design consists of, and how quality of the study design influences analysis of data and trustworthiness of data-insights. In line with this, students learn about sampling theory, missing-data mechanisms, how to handle missing data, and the impact that missing data has on usefulness of interpretations of Descriptive Statistics and Inferential Statistics. Furthermore, students become familiar with labyrinths of Inferential Statistics and learn about differences between predictive analytics, causal effect analytics and impact evaluations.