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challenges in learning from experiments
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Classroom Contents
Using Theories of Decision-Making Under Uncertainty to Improve Data Visualization
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- 1 Intro
- 2 data summaries for inductive inference
- 3 objective: perceptual accuracy?
- 4 good perception = rational judgment?
- 5 objective: pattern finding
- 6 optimizing for pattern finding encourages NHST?
- 7 minimize error in effect size judgment/decisions
- 8 non-robust strategies → illusion of predictability
- 9 The distance heuristic
- 10 when optimizing for PoS isn't enough...
- 11 challenges in learning from experiments
- 12 when does a better visualization matter?
- 13 defining a decision problem (from Kale et al. 2020)
- 14 dead in the water (Gelman and Weakliem 2009)
- 15 post-experiment: rank behavioral agents with vis
- 16 post-experiment: rank heuristics
- 17 design applications: aggregation choices
- 18 what characterizes a good interfaces problem?
