CS 780 Critical Thinking for the Digital Age (Fall 2022)

Schedule: Slot 6, Venue: CC 101

Moodle: Slides, Assignments, Solution, Newsgroup etc.

Everyday we receive tons of data, graphs, and arguments. How do we decide which of these data are consistent, information is reliable and arguments are valid? Fake news does not come only in form of manufactured news but often comes in the form of misleading inferences from a careful selection of misleading data. Critical thinking is needed not to deal with malicious information alone. As society deals with big challenges such climate change, developmental challenges, and corona epidemic, we have to deal with varied and often contradictory views, all of which are genuine concerns and opinions from stakeholders with different perspective. We have to synthesize information from these genuinely competing information sources also. All these pitfalls and errors are not only for observing in other people's work but will also help us avoid them in our own work. In this context, the course will set up the theoretical background needed and look at number of real life case studies. The course will not be technology heavy. It will be theoretical in the sense of focusing on analysis and not on tools, but will be practical in the sense of using large number of real examples. We will use various online tools and calculators wherever relevant but that will not be the main focus of the course.

Topics Covered:

The Scientific Method:
    Induction (Generalization from Observations) and Deduction (Prediction)
    Experiments, Predictions and Falsification
    Testing Theoretical Hypotheses : Elements of a good test
    Fallacies of Theory Testing: vague predictions, multiple predictions, no predictions, justification by elimination
Logic: Formal and Informal – interspersed throughout the course
    Valid and Invalid Deductions and Inductions
    Fallacious Reasoning: Inconsistency, False Dilemma and the Either-Or Fallacy, Begging the Question, Overlooked Evidence, Irrelevant Reason, Two wrongs make a right
    Evaluating Extended Arguments
Probability: Basic Foundations, Bayes Theorem as basis for Abduction (from Effect to Cause)
Statistical Reasoning:
    Correlation (things that happen together) and Causation (cause and effect relationship)
    Testing Statistical and Causal Hypotheses
    Common Errors: Pseudo-correlation, Pseudo-replication, P Values and Base Rate Fallacy, Double Counting and  Circular Reasoning, Missing Data

Systems Thinking: context of everything, system in which seemingly independent events occur, feedback from our actions, what else is affected
Conservation Law: often gets ignored

Counterfactual: What if something was not done
Role of Units: Quantifying qualitative statements

But vs. Therefore: paradoxes as their own explanations
Misleading Visualization

Perils of Big Data
Biases of Machine Learning

Reference

• What Is This Thing Called Science?, Alan Chalmers. Open University press, 4th edition, 2013
• Understanding Scientific Reasoning, Ronald N. Giere et. al. Cengage Learning, 2005.
• Logic \& Contemporary Rhetoric, Nancy Cavender and Howard Kahane. Cengage Learning 2014.
• Statistics Done Wrong, Alex Reinhart. No Starch Press 2015.
• Seeing Through Statistics, Jessica M. Utts. 4th Edition. Cengage Learning, 2015.
• Calling Bullshit: The Art of Skepticism in a Data-Driven World, by Carl T. Bergstrom, Jevin D. West. Random House, 2020.

Pre-requisites: None

Audit Requirements: You have to do all the assignments. There will be at least one assignment per week.