Offers an introduction to the scientific method and common biases in science. Examines theoretical considerations and practical examples where biases have led to erroneous conclusions, as well as scientific practices that can help identify, correct or prevent such biases. Additionally focuses on appropriate methods to interweave inductive and deductive approaches. Topics covered include: Popper's falsification and Kuhn's paradigm shift, revolution vs. evolution; determinism and uncertainty; probability, hypothesis testing, and Bayesian approaches; agnostic testing and big data; team science; peer review; replication; correlation and causation; bias in design, analysis, reporting and sponsorship of research; bias in the public perception of science, mass media and research; and bias in human history and everyday life. Provides students an understanding of how scientific knowledge has been and will be generated; the causes of bias in experimental design and in analytical approaches; and the interactions between deductive and inductive approaches in the generation of knowledge.
3 units · Letter or Credit/No Credit · GER: WAY-SMA
Offers an introduction to the scientific method and common biases in science. Examines theoretical considerations and practical examples where biases have led to erroneous conclusions, as well as scientific practices that can help identify, correct or prevent such biases. Additionally focuses on appropriate methods to interweave inductive and deductive approaches. Topics covered include: Popper's falsification and Kuhn's paradigm shift, revolution vs. evolution; determinism and uncertainty; probability, hypothesis testing, and Bayesian approaches; agnostic testing and big data; team science; peer review; replication; correlation and causation; bias in design, analysis, reporting and sponsorship of research; bias in the public perception of science, mass media and research; and bias in human history and everyday life. Provides students an understanding of how scientific knowledge has been and will be generated; the causes of bias in experimental design and in analytical approaches; and the interactions between deductive and inductive approaches in the generation of knowledge.
Offered in Winter 2027 at Stanford University.