Biology Experimental Design
6 weeks · 0 milestones
Design a real biological investigation from scratch: state a testable hypothesis, identify independent and dependent variables, define controls, justify sample size using a power analysis or reasoning from published literature, and document ethics considerations for living organisms or human participants. The proof is the complete experimental design document reviewed and approved by a working biologist BEFORE data collection begins — the biologist confirms that the design is methodologically sound and the hypothesis is genuinely testable. For students without lab access, the design may be for a secondary data analysis study using a named publicly available dataset (GBIF, NCBI SRA, iNaturalist) — the design document requirements are identical: hypothesis, variables, sampling methodology, statistical analysis plan. The design precedes data collection; this is a genuine prerequisite, not a formality.
Milestone map
Milestone map
3 milestones
Define a testable biological hypothesis and design the experiment that will test it. Accessible alternative: computational experiments (simulations, mathematical models, re-analysis of a published raw dataset using a novel design approach) are fully valid for this outcome — field or wet-lab access is not required. The proof standard is identical for both routes.
Proof required
Submit a written experimental protocol (2–4 pages) including: your null and alternative hypotheses, your independent and dependent variables, all controlled variables, your replication strategy, a brief statistical power justification (why your chosen sample size is adequate), and the analysis you plan to run on the results.
What gets checked
- Null hypothesis is falsifiable and clearly stated — not 'I expect to find a difference' but 'there is no significant difference in [variable] between [condition A] and [condition B]'
- All three variable types are named (independent, dependent, controlled) with specific operationalised definitions
- Statistical power justification is present — even a brief note ('n=10 per group based on a published effect size of d=0.8 at 80% power') demonstrates the submitter understands why sample size matters