Methods: Overview, resources
Sections
"Qualitative" design issues: How to design the 'content' of experiments and surveys to have internal validity and external generalizability
Real-world assignment & inference: How to set up trials to have comparable groups
Adaptive design/sampling, reinforcement learning: Adjusting the treatments and design as you learn, to 'get to the highest value in the end'
Analysis: Statistical approaches: How to make inferences from the data after you have it (and plan this in advance)
What are our estimation goals?
Statistical power versus optimized learning
Fixed vs adaptive designs
Resources
Rethink Priorities notes (some are works in progress)...
The https://declaredesign.org/ framework and R package seems very helpful. I (David Reinstein) am learning and trying to adapt it.
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