Anderson-Darling Goodness-of-Fit Test
The Anderson-Darling (A²) test gives higher weight to the distribution tails than the Kolmogorov-Smirnov test, making it the preferred industrial test for quality engineering.
Intermediate Category: Process Capability & Measurement (MSA) • ~6 min read
Normality Assessment & Probability Plot
Verify statistical assumptions before executing capability studies, ANOVA, or hypothesis tests.
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.
Q:
Why must normality be verified before calculating Process Capability (Cp/Cpk)?
Standard capability indices (Cp, Cpk) mathematically assume a normal Gaussian distribution. If data is skewed or bimodal, standard 6σ formulas drastically underestimate expected defect rates (PPM).
Anderson-Darling (A²)0.232Goodness-of-Fit
P-Value0.7702Threshold α = 0.05
Sample Skewness-0.010 = Symmetric
Normality StatusNormally DistributedH0: Data is normal
!
Engineering Disclaimer:
These tools and calculators are intended for engineering analysis, educational study, and operational guidance. They do not constitute formal regulatory certification (such as medical, aerospace flight-critical, or statutory safety filings) without independent audit and sign-off by a certified Quality Engineer or Six Sigma Black Belt. AshToolbox is not affiliated with, sponsored by, or endorsed by AIAG, ISO, ASQ, or any standards body.
Recommended Next Steps in Quality Workflow
Process Capability & Measurement (MSA)
Process Capability Analysis (Cp, Cpk, Pp, Ppk, Cpm)
Determine whether a manufacturing process complies with customer engineering tolerances and specifications.
Statistical Process Control (SPC)Individuals and Moving Range (I-MR) Chart
Establish control limits for low-volume production, destructive tests, and batch chemical processes.
Statistics for ImprovementHypothesis Testing Decision Wizard & Solver
Statistically validate process improvements and determine whether differences in mean or variance are statistically significant.