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