Welcome!
Welcome to the SSS wiki! This site holds the lesson plans, projects, teaching guides, and content for the UC Berkeley course Sense and Sensibility and Science. Although this wiki is written to be used by instructors at UC Berkeley, it can easily be adapted for use at other universities. Variants of SSS have already been taught at Harvard and UC Irvine and will soon be taught at UChicago as well!
You will need an account to view or edit pages on this wiki. Please log in or request an account. Please direct any inquiries to Winston Yin and Gabriel Perko-Engel.
About the Course
This is a course on the ideas from science that are most widely useful for everyone. Many insights and conceptual tools from scientific thinking are of great utility for all kinds of reasoning, from reading the news critically to making decisions under conditions of uncertainty. The focus in this course is on the errors humans tend to make, and the approaches scientific methodology has developed (and continues to develop) to minimize those errors. The course includes a discussion of the nature of science, what makes science such an effective way of knowing, how both non-scientific thinking and scientific thinking can go awry, and how we can reason more clearly and successfully as individuals, as members of groups, and as citizens of a democracy.
Every day we make decisions that can and should be informed by science. We make decisions as individuals, as voters, and as members of our various communities. We make decisions as students and parents and policy makers. The problem is, we don't do it so well—a fact sadly apparent in political debates. It's easy to blame poor decision-making on the greed, irresponsibility, ignorance, or incompetence of other people. But the problem seems to be more basic than that. It seems we face a paradox. Living in a democracy means that everyone's view counts the same as everyone else's. But to make decisions informed by science, we often need to defer to those with relevant expertise. Therefore, we shouldn't rely on a democratic system to make the best decisions. Or should we? This basic tension between science and democratic decision-making serves as a unifying theme for Sense & Sensibility & Science (SSS), a course that aims to equip students with basic tools to be better thinkers. We will explore key aspects of scientific thinking that everyone should know, especially the many ways that we humans tend to fool ourselves, and how to avoid them—including how to differentiate signal from noise, evaluate causal claims, and avoid reasoning biases. We'll then look at the best models for using science to guide decisions, since the rational and arational (e.g., values, fears, and goals) then have to be combined. We will explore these themes experientially, often with in-class activities and discussions, and we will culminate in two open-ended projects to design better methods of deliberation and decision making, first as groups, and then as individuals. Co-taught by faculty from Physics, Philosophy, and Psychology, S&S&S fosters intellectual advancement for interdisciplinary knowledge seekers. At UC Berkeley, this course (L&S 22) satisfies the Philosophy and Values, Physical Science, or Social and Behavioral Sciences breadth requirements in the College of Letters & Science.
Course Content
The full version of this course is fourteen weeks long and covers topics from philosophy, psychology, data science, and the natural sciences. The topics in this course are as follows. You will need an account to see the topics and lesson plans in more depth.
- 1.1 Introduction and When Is Science Relevant
- Democracy vs. epistocracy
- Facts vs. values
- 1.2 Shared Reality and Modeling
- Shared reality
- Raft vs. pyramid
- Evaluation of models
- Science vs. decree
- Scientific realism vs. anti-realism
- Operationalism, conventionalism, and realism
- 2.1 Senses and Instrumentation
- Validation of instruments through interactive exploration, triangulation with other instruments, and comparison with direct senses
- 2.2 Systematic and Statistical Uncertainty
- Measurement proxies as sources of systematic and statistical uncertainty
- 3.1 Probabilistic Reasoning & 3.2 Calibration of Credence Levels
- The value of partial and probabilistic information
- Words of estimative probability (probably, likely, definitely, etc.)
- [math]\displaystyle{ p }[/math]-values and statistical significance
- Error bars and confidence intervals
- Strategies to improve calibration of credence levels (feedback, AOT, growth mindset, etc.)
- 4.1 Signal and Noise & 4.2 Finding Patterns in Random Noise
- Signal-to-noise ratio
- [math]\displaystyle{ p }[/math]-hacking
- Look elsewhere effect
- Gambler's fallacy
- Hot-hand fallacy
- File drawer effect
- HARKing (hypothesizing after results are known)
- Effect size (as distinct from statistical significance)
- 5.1 False Positives and Negatives
- Thresholds between positive and negative detections
- Trade offs between false positives and negatives
- 5.2 Scientific Optimism
- Iterative progress
- 6.1 Correlation and Causation
- Causation as correlation under intervention
- Randomized controlled trials
- Different directions of causation
- Spurious correlations
- 6.2 Hill's Criteria (Causation in the Messy Real World)
- Natural experiments
- Hill's criteria for causation
- Causal networks
- 7.1 Causation, Blame, and Policy
- Singular and general causation
- Acts of omission vs. commission (and the omission bias)
- Status quo bias
- 7.2 Emergent Phenomena
- Global effects that arise through the interaction of small pieces (rather than general causation)
- Explanation at different scales
- Scientific reductionism
- 8.1 Orders of Understanding
- Orders of magnitude
- Multiple causes of comparable importance
- Orders of importance of causes
- Refinement of models using higher order descriptions
- Scale of impact of policies
- 8.2 Fermi Problems
- Fermi problems
- 9.1 Heuristics
- Base rate neglect
- Representativeness heuristic and conjunction fallacy
- Availability heuristic
- Bounded rationality
- 9.2 Biases
- Fundamental attribution error
- Conformity
- Obedience
- Temporal discounting
- 10.1 Confirmation Bias
- Selective exposure
- Biased assimilation
- Strategies for reducing confirmation bias (AOT, etc.)
- 10.2 Blinding
- Techniques for blind analysis
- Preregistration
- Registered replication
- Adversarial collaboration
- Peer review
- 11.1 Pathological Science
- The spectrum of poor research
- Langmuir's pathological science indicators
- 11.2 When Is Science Suspect
- The validity and reliability of social science metrics
- External validity
- The difficulty of creating and applying social science metrics in cross-cultural contexts
- How researchers' biases and backgrounds shape their research agendas
- 12.1 Wisdom of Crowds and Herd Thinking
- The Wisdom of Crowds effect removing independent biases via error cancellation
- How humans can engage in herd thinking to cluster around biased answers to group questions
- 13.1 Denver Bullet Study
- The Denver Bullet Study process of integrating factual analysis from experts with value ratings from different stakeholders
- 13.2 Deliberative Polling
- The deliberative polling process of having the general public make decisions as though they were experts
- 14.1 Scenario Planning
- The Scenario Planning process of predicting and responding to possible futures
- What makes good drivers to use in Scenario Planning