08. Experiment Design

AI-assisted experiment design leverages accumulated knowledge to propose optimal experimental approaches. These systems suggest specific protocols, parameter ranges, and control conditions based on prior research.

Literature-based design extracts experimental parameters from published methods. What sample sizes achieved statistical significance? Which control conditions were essential? What measurement techniques produced reliable results? Aggregating this information provides empirically-grounded starting points.

Optimization approaches identify parameter combinations most likely to succeed. Bayesian optimization balances exploration of unknown regions with exploitation of promising areas. Multi-objective optimization considers multiple success criteria simultaneously. Adaptive designs adjust mid-experiment based on emerging results.

# Bayesian optimization for experimental parameters
from skopt import gp_minimize
from skopt.space import Real, Integer

def optimize_experiment(objective_function, parameter_space):
    # Define search space based on literature review
    dimensions = [
        Real(parameter_space['temp_min'], parameter_space['temp_max'], name='temperature'),
        Integer(parameter_space['conc_min'], parameter_space['conc_max'], name='concentration'),
        Real(parameter_space['time_min'], parameter_space['time_max'], name='duration')
    ]
    
    result = gp_minimize(
        func=objective_function,
        dimensions=dimensions,
        n_calls=50,
        random_state=42,
        noise='gaussian',
        n_initial_points=10
    )
    return result

Statistical power analysis ensures experiments can detect meaningful effects. Literature-derived effect size estimates inform sample size calculations. Sequential analysis allows stopping rules when sufficient evidence accumulates. Multiple comparison corrections prevent false positive inflation.

Simulation-based design tests hypotheses before execution. Virtual experiments identify flawed designs before resources are spent. Sensitivity analysis reveals which parameters most affect outcomes. Monte Carlo methods estimate success probability under uncertainty.

Documentation generation creates detailed protocols from design specifications. Standard operating procedures ensure reproducibility. Version control tracks protocol evolution. Integration with laboratory information management systems automates execution.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.