IntroSems quarters and schedules subject to change--check back often. Go to Re-Approaching Stanford for weekly updates on Academic Year 2020-21.
Sign up for priority enrollment in Winter IntroSems in the IntroSems' VCA between October 16th and November 13th at 8AM PT. Winter status will be released by December 4th.
Introductory Seminars for First-Year Students
Scientific Method and Bias
This seminar 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. We will also examine appropriate methods to interweave inductive and deductive approaches.
Over the past 50 years, remarkable advances in biomedical science and other scientific fields such as genomics have been steered by hypothesis-driven research. But there have been many setbacks and false conclusions because experiments were not properly designed or data were misinterpreted or improperly analyzed. For example, researchers observed the same19 e abnormal gene expression in a 50-year old obese man and some of his biological relatives who were also obese. They concluded that the abnormal gene expression causes obesity. This example demonstrates that inference cannot lead to valid conclusions about causation, as we do not know the social, environmental, or other biological reasons that may influence the family’s predisposition to obesity.
The seminar will cover the following topics: 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.
At the end of the seminar, you will have 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.