design-biological-experiment

Design Biological Experiment

Structure a biological experiment with controls, replication, and statistical power to produce valid, reproducible results.

Why This Is Best Practice

Adopted by: OECD member nations (GLP compliance), NIH-funded research programs, Cold Spring Harbor Laboratory, peer-reviewed journals requiring ARRIVE/CONSORT reporting.

Impact: Fisher's randomized block design reduced experimental error by 30–50% in agricultural trials; GLP-compliant studies have a 60% lower rate of retraction vs. non-compliant studies (Fanelli 2012).

Why best: Randomization eliminates selection bias; replication separates signal from noise; blinding prevents observer bias — together these are the minimal conditions for causal inference in biology.

Sources: OECD GLP Principles (ENV/MC/CHEM(98)17); Fisher (1935); Cold Spring Harbor Protocols experimental design series.

Steps

1. State the hypothesis — write a single falsifiable statement in IF-THEN-BECAUSE form (e.g., "If gene X is knocked out, then cell proliferation will decrease by >20%, because X activates the MAPK pathway").

2. Identify variables — list independent variable (what you manipulate), dependent variable (what you measure), and all confounders you will control.

3. Define controls — include a positive control (known outcome), negative control (no treatment), and vehicle control (solvent only) for every experimental group.

4. Calculate sample size — use power analysis (α=0.05, β=0.20, effect size from pilot or literature) before starting; target ≥80% power. See calculate-statistical-power.

5. Assign randomization — randomly assign subjects/samples to groups using a random number table or software (R, Python) to prevent systematic bias.

6. Plan blinding — blind the experimenter to group assignment during measurement wherever feasible; use coded labels.

7. Write the protocol — document each step with exact reagent concentrations, instrument settings, timing, and acceptance criteria for data quality.

8. Specify statistical analysis — pre-register the primary statistical test, multiple-comparison correction method, and exclusion criteria before data collection.

9. Execute and record — record all deviations from protocol in a lab notebook contemporaneously; photograph key results.

10. Validate reproducibility — replicate the key experiment ≥3 times on separate days (biological replicates, not just technical replicates).

Rules

• Never pool technical replicates as if they were biological replicates — they do not increase statistical power.
• Pre-register hypothesis and analysis plan before data collection to prevent HARKing (Hypothesizing After Results are Known).
• Include both positive and negative controls in every assay run, not just the initial validation.
• Report all exclusions and deviations — omitting failed runs is a form of reporting bias.

Common Mistakes

• Pseudoreplication — treating technical replicates as biological replicates inflates n and produces false precision.
• No positive control — without a positive control, a negative result cannot be distinguished from assay failure.
• Post-hoc hypothesis — fitting the hypothesis to observed data destroys Type I error control and is a form of p-hacking.
• Ignoring batch effects — running treatment and control groups on different days or with different reagent lots introduces confounders.

When NOT to Use

• When performing purely observational or descriptive studies (use appropriate survey or cohort design instead)
• When ethical or feasibility constraints prevent randomization (use quasi-experimental design with explicit limitation disclosure)
• When the research question is exploratory/hypothesis-generating (use pilot study design with no inferential statistics)