Case Studies
Real examples of experiments run through Litmus, covering different user types, budgets, and outcomes.
Featured Case Studies
| Case Study | User Type | Budget | Outcome |
|---|---|---|---|
| Honey Remedy | Citizen scientist | $200 | Hypothesis partially supported |
| AI Drug Screening | Biotech startup | $12,000 | 3 hits identified |
| Operator Perspective | PhD student | — | $800-1,200/month earned |
| Failed Hypothesis | Software engineer | $400 | Hypothesis rejected |
Why Case Studies Matter
These case studies demonstrate:
- Different user personas: From hobbyists to AI pipelines
- Various budget levels: 12,000+
- Both positive and negative results: Science isn't just about confirming hypotheses
- The operator perspective: How the platform works for those running experiments
Key Takeaways
- Start with what's testable — You can't always test your ultimate question, but you can test the underlying mechanism
- Protocol design assistance works — You don't need to know the exact assay to get started
- Negative results are data — A rejected hypothesis is still valuable information
- Structured output enables automation — AI pipelines can integrate directly via webhooks
- Operators benefit too — Graduate students can monetize skills they already have