COURSE CONTRIBUTION TO PROGRAM
This course focuses less on technical aspects of data analysis and more on conceptual ones. Thinking with data is what we do before and after running the statistical tools students learn in other courses. The course content is primarily based on the following three sources of information:
(1) Psychological research on people's incorrect intuitions about data and randomness,
(2) Practical tools empirical researchers have developed, mostly in medicine, economics, and political science, to make firmer causal inferences from data. These tools are useful also for everyday decision making in organizations but solve problems that are typically assumed away in traditional statistical textbooks and courses, and
(3) Foundational statistical concepts which are also useful for everyday decision making in organizations but which are usually too abstractly covered to be implemented in everyday practice (e.g., statistical power, practical vs statistical significance, concluding something does not exist vs failing to conclude it does, etc.).
Course Learning Objectives
Improve students' ability to:
1) Figure out what is the right way to ask a given question from data, and
2) Understanding what question a given analysis is actually answering.
For example, say a company rolls out a promotion to increase sales. Comparing sales before and after the promotion does not tell us if it was successful.
This course helps students better understand what question the before after comparison does answer, and how to adequately asses if the promotion worked.
CONTENT
1. Content 1) Statistical bootstrapping (to conceptually understand blackbox solutions in classical stats and to carry out analyses for which formulas do not exist) 2) How to evaluate if an intervention was successful? 3) The challenge specifying the question after we see the data (that vs likethat and mutiple comparisons) 4) Experiments as information 5) Diagnosing and correcting for selfselection 6) Disentangling skill from luck in performance 7) Forensic statistics (how to detect errors, selective reporting, and fraud) 8) Learning from errors and closecalls

Methodology
Lecture discussion and handson analysis of data in class. We will often contrast how students solved a (practical, realworld) problem before coming to class with how the content covered suggests we cover it.