💡
EA market testing (public)
  • Introduction/overview
    • Introduction & explanation
    • 👋Meet the team
    • 📕Content overview
    • Progress/goals (early 2023)
      • EAMT progress & results
      • Goals, trajectory, FAQs
  • 🤝Partners, contexts, trials
    • Introduction
    • Giving What We Can
      • Pledge page (options trial)
      • Giving guides - Facebook
      • Message Test (Feb 2022)
      • YouTube Remarketing
    • One For the World (OftW)
      • Pre-giving-tues. email A/B
        • Preregistration: OftW pre-GT
    • The Life You Can Save (TLYCS)
      • Advisor signup (Portland)
    • Fundraisers & impact info.
      • ICRC - quick overview
      • CRS/DV: overview
      • 📖Posts and writings
    • University/city groups
    • Workplaces/orgs
    • Other partners
    • Related/relevant projects/orgs
  • 🪧Marketing & testing: opportunities, tools, tips
    • Testing Contexts: Overview
    • Implementing ads, messages, designs
      • Doing and funding ads
      • Video ads/Best-practice guidelines
      • Facebook
      • Targeted ad on FB, with variations: setup
    • Collecting outcome data
      • Facebook ads interface
        • Pivot tables
      • Google analytics interface
      • Google A/B, optimize interface
      • Reconciling FB/GA reports
      • Survey/marketing platforms
    • Trial reporting template
  • 🎨Research Design, methodology
    • Methods: Overview, resources
    • "Qualitative" design issues
    • Real-world assignment & inference
      • Geographic segmentation/blocked randomization
      • Difference in difference/'Time-based methods'
      • Facebook split-testing issues
    • Simple quant design issues
    • Adaptive design/sampling, reinforcement learning
    • 'Observational' studies: issues
    • Analysis: Statistical approaches
  • 🧮Profiling and segmentation project
    • Introduction, scoping work
    • Existing work/data
      • Surveys/Predicting EA interest
      • Awareness: RP, etc.
      • Kagan and Fitz survey
      • Longtermism attitudes/profiling
      • Animal welfare attitudes: profiling/surveying
      • Other data
    • Fehr/SOEP analysis... followup
      • Followup with Thomas Ptashnik
    • Further approaches in progress
      • Profiling 'existing traffic'
  • 📋(In)effective Altruistic choices: Review of theory and evidence
    • Introduction...
    • The challenge: drivers of effective/ineffective giving
      • How little we know...
    • Models, theories, psych. norms
    • Tools and trials: overview
      • Tools/interventions: principles
      • Outcomes: Effective gift/consider impact)
        • (Effectiveness information and its presentation)
        • (Outcome: Pledge, give substantially (& effectively))
          • (Moral duty (of well-off))
        • Give if you win/ conditional pledge
      • Academic Paper Ideas
  • Appendix
    • How this 'gitbook' works
      • Other tech
    • Literature: animal advocacy messaging
    • Charity ratings, rankings, messages
    • "A large-scale online experiment" (participants-aware)
  • Innovationsinfundraising.org
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  • Impact of treatment on 'rare event' incidence
  • Open and robust science: Preregistration and Preanalysis plans
  • Which statistical tests/methods
  • From Sample to Population: Multilevel Regression and Poststratification (MRP) and Survey Weighting

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  1. Research Design, methodology

Analysis: Statistical approaches

What to do with the data after you collect it (and what you should put in a pre-analysis-plan).

Previous'Observational' studies: issuesNextIntroduction, scoping work

Last updated 2 years ago

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Impact of treatment on 'rare event' incidence

Notes from slack:

I’m finding some issues like this in analyzing rare events … not quite that rare, but still a few per thousand or a few per hundred.

I’m taking 2 statistical approaches to the analysis (discussion, code, and data in links):

  1. Randomization inference (simulation) … for a sort of

I think either of these could be ‘flipped around’ to be used for power calculation or ‘the Bayesian equivalent of power calculation’

My colleague Jamie Elsey has some expertise with the latter; , although it’s mainly frequentist and not Bayesian ATM.

Open and robust science: Preregistration and Preanalysis plans

There are reasons 'some pre-registration' or at least 'declaring your intentions in advance' is worth doing even if you aren't aiming at scientific publication

Which statistical tests/methods

🎨
Bayesian binomial-beta (a pretty standard setup I’m probably making overcomplicated)
equivalence testing here
we’re putting together our discussion HERE
https://gitlab.com/dsbowen/conditional-inference/-/blob/master/examples/bayes_primer.ipynb
From Sample to Population: Multilevel Regression and Poststratification (MRP) and Survey Weighting
https://docs.google.com/document/d/14uTZqOpnKAK8_oRwgqlAhEXfyQPQUKK9/editdocs.google.com