# Methods: Overview, resources

## Sections

["Qualitative" design issues](/untitled/methodological-discussion/qualitative-design-issues.md): How to design the 'content' of experiments and surveys to have internal validity and external generalizability

[Real-world assignment & inference](/untitled/methodological-discussion/splits-randomization.md): How to set up trials to have comparable groups

[Adaptive design/sampling, reinforcement learning](/untitled/methodological-discussion/adaptive-design-sampling-reinforcement-learning.md): Adjusting the treatments and design as you learn, to 'get to the highest value in the end'

[Analysis: Statistical approaches](/untitled/methodological-discussion/analysis-statistical-approaches.md): 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](/untitled/methodological-discussion/adaptive-design-sampling-reinforcement-learning.md)

## Resources

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

{% embed url="<https://rethinkpriorities.github.io/methodology-statistics-design/introduction-and-overview.html>" %}
RP notes on methods
{% endembed %}

{% embed url="<https://rethinkpriorities.github.io/how-to-visualize>" %}
RP dynamic doc on making graphs etc
{% endembed %}

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

[Dillon's 'Hemlock'](https://dsbowen.github.io/software/hemlock/)

[Reinstein 'research tools and data' airtable list](https://airtable.com/invite/l?inviteId=inv9DBIxRLVztaO8U\&inviteToken=82dc3cdefbad836030c92c23974629c02f0aeb0e93b773c58bb8c7e17d4fa6d6)


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