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

See adaptive design notes

Resources

Rethink Priorities notes (some are works in progress)...

https://rethinkpriorities.github.io/methodology-statistics-design/introduction-and-overview.htmlrethinkpriorities.github.io

RP notes on methods

How to Visualizerethinkpriorities.github.io

RP dynamic doc on making graphs etc

The https://declaredesign.org/ framework and R package seems very helpful. I (David Reinstein) am learning and trying to adapt it.

Dillon's 'Hemlock'

Reinstein 'research tools and data' airtable list