Experimental Design Guide

A skill for designing rigorous experiments using formal Design of Experiments (DOE) methodology. Covers factorial designs, fractional factorials, response surface methods, and optimal design strategies for scientific research.

Fundamental Principles

Fisher's Three Principles

1. Randomization: Assign experimental units to treatments randomly to eliminate systematic bias
2. Replication: Include enough replicates to estimate experimental error and ensure statistical power
3. Blocking: Group similar experimental units to reduce nuisance variability

Sample Size and Power Analysis

from scipy import stats
import numpy as np

def power_analysis_ttest(effect_size: float, alpha: float = 0.05,