Ensure quality experiments and validation audits collect enough data to achieve desired statistical power without wasting testing budget.
Client-Side Privacy Guarantee
The data you enter is processed in your browser and is not uploaded. Ads and analytics are provided by Google separately.
How is required sample size determined for hypothesis tests and experiments?
Sample size n depends on Significance level α (Type I error, typically 0.05), Statistical Power 1-β (Type II error, typically 0.80 or 0.90), Minimum detectable effect δ, and Process variation σ. Limits are calculated using standard normal critical values z_α/2 and z_β.
n = ((z_α/2 + z_β) × σ / δ)²
32
Total units requiredUnderpowered studies risk concluding no process difference exists when a real improvement occurred (false negative). Computing sample size in advance guarantees statistical sensitivity before investing time and capital into trials.
Statistically validate process improvements and determine whether differences in mean or variance are statistically significant.
Acceptance SamplingGenerate mathematically defensible inspection plans with complete OC, AOQ, AOQL, and ATI curves.
Statistics for ImprovementScreen critical process factors and discover interaction effects using orthogonal experimental arrays.