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Design rigorous experiments using DOE, factorial designs, and response surfaces
Passed 17 of 24 test cases with the skill, and 17 of 24 without it.
0 fixed17 passed either way7 still failing0 broke
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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.
from scipy import stats
import numpy as np
def power_analysis_ttest(effect_size: float, alpha: float = 0.05,
power: float = 0.80, ratio: float = 1.0) -> dict:
"""
Calculate required sample size for a two-sample t-test.
Args:
effect_size: Cohen's d (expected effect size)
alpha: Significance level
power: Desired statistical power
ratio: Ratio of n2/n1 (for unequal groups)
"""
from statsmodels.stats.power import TTestIndPower
analysis = TTestIndPower()
n1 = analysis.solve_power(
effect_size=effect_size,
alpha=alpha,
power=power,
ratio=ratio,
alternative='two-sided'
)
return {
test cases passed, vs 17 / 24 without
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