Students will learn:
* How to distinguish a good research design from a bad research design
* How to distinguish good empirical tests from bad empirical tests
* How to interpret research findings and reflect on the generalizability of data
* How to effectively communicate about research to policy makers and laymen
* How to develop products or design policies based on research data

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.

There is no doubt that well-performed, data-driven research has the potential to improve the design of services and policies. However, many potential points of failure could hamper the journey from data to policy. This course focuses on a particular segment of this journey: the connection between the researcher and the decision maker. This is highly relevant because services and policies often need to rely on existing research results.

A key capacity of the decision maker is to be able to critically reflect on scientific research, to identify weaknesses or even severe problems in the research design. At the same time, researchers in data science need to learn to effectively communicate research findings to policy makers and media, often laymen in data science. Hereby scientists need to get attention for their research, while forgoing wrong interpretations at the same time. You will therefore also learn to detect "lying with statistics" and how to prevent such problems. This course will thus give you the necessary skills to apply data-driven research results in your career in policy, academia and business, and to communicate your findings in a way that they are correctly applied.