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Methods: Overview, resources


​"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


Rethink Priorities notes (some are works in progress)...
How to Visualize
RP dynamic doc on making graphs etc
The framework and R package seems very helpful. I (David Reinstein) am learning and trying to adapt it.
​Dillon's 'Hemlock'​