Variance Homogeneity: Distribution Shape Matters to the Test Choice
An equal-variance test depends on the distributional assumptions behind it. A different outcome can reflect method sensitivity rather than an arithmetic error.
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A Two-Variance F Ratio Uses Squared Standard Deviations
When comparing two spreads with an F ratio, retain squared units, numerator identity and the degrees of freedom associated with both samples.
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One-Way ANOVA: An Overall Result Does Not Name the Different Pair
An overall group-mean test can show evidence of a difference somewhere without identifying every pair. Preserve that scope in the trial conclusion.
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Fixed and Random Factor Levels: Define Where a Trial Conclusion Applies
Comparing particular settings and estimating variability across a wider population of batches require different inference scopes, even with similar-looking tables.
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Defect-Type Contingency Tables: Compare Observed Counts with Independence
A defect-type-by-source table asks whether classifications are associated. Preserve cell counts and expected counts instead of comparing percentages without their totals.
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Bootstrap Resampling: More Resamples Do Not Create More Physical Trials
Bootstrap draws reuse the observed data. A large number of computational resamples should never be reported as a large number of independent machine trials.
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Changing a Machine Order: Review the Scope Impact Before Approval
Review a post-order machine change against the accepted scope, affected interfaces, trial and document needs, and the supplier's revised confirmation.
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Mean and Median: State Which Typical Filling Result Is Being Reported
A mean and a median answer different summary questions. Name the statistic when sharing a typical reading, especially when a trial sample has an asymmetric tail.
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Median Absolute Deviation: Identify the Raw or Scaled Convention
A report labeled MAD may use a raw median absolute deviation or an adjusted value. Retain the exact definition before comparing the robust spread with a standard deviation.
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Kurtosis Reports: Check Whether the Software Subtracts Three
A three-unit difference between kurtosis outputs can be a definition change. Check the convention and finite-sample adjustment before attributing it to process behavior.
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A Runs Test Uses Sequence Changes, Not the Magnitude of Each Reading
A median-based runs test counts stretches of the same binary label in physical order. Sorting the readings before counting changes the question.
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Kolmogorov-Smirnov Tests: Fitted Parameters Change the Reference Problem
A standard Kolmogorov-Smirnov comparison needs a fully specified continuous reference distribution. Fitting its parameters from the same data changes the calibration.
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Anderson-Darling Reports: The Reference Distribution and Critical Values Matter
Anderson-Darling output needs the tested distribution and its matching statistic adjustment. A numerical cutoff copied from another model can mislead the review.
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Chi-Square Goodness of Fit: Expected Bin Counts and Estimated Parameters Matter
A binned goodness-of-fit test needs declared bin boundaries, model probabilities and parameter-estimation treatment. A frequency table alone does not identify the test.
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Coded Factor Levels: Minus One and Plus One Need Their Physical Settings
Factorial codes are coordinates in a design, not machine settings. Translate every coded row using the correct factor-specific units before discussing a trial.
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Center-Point Runs: Look for Curvature Before Trusting a Two-Level Fit
Center responses can reveal a curvature question that a two-level fit misses. The comparison must still account for variability and changes during the experiment.
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Quadratic Trial Models: Squared Terms Need Their Own Evidence
Quadratic models add squared-factor terms to represent curvature. Seeing an interaction or adding a center point does not establish that each curvature term can be fitted.
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Central Composite Star Points: Review the Proposed Physical Extremes
A proposed central composite plan can introduce factor settings beyond earlier low/high levels. Review the physical axial points before treating a matrix as executable.
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Box-Behnken Designs: A Missing Cube Corner Was Not Tested
A three-factor Box-Behnken plan uses edge-midpoint and center combinations. Its data should not be described as direct observations of every simultaneous extreme.
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Three-Level Full Factorials: Count the Combinations Before Scheduling
A three-level full factorial grows as three raised to the number of factors. Count all combinations, repetitions and practical preparation work before booking the trial.
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Welch Comparisons: Unequal Group Sizes Need the Actual Variance Contributions
An independent-mean comparison with unequal spreads keeps each group variance contribution separate. One shared spread number can hide the intended method.
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Lack-of-Fit Tests: Retain Replicates at the Same Predictor Settings
Replicates at identical predictor settings provide a pure-error reference for lack-of-fit analysis. Unique settings alone cannot supply that same comparison.
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Raster Pixels and Print Size: Keep Image PPI Separate from Printer DPI
Calculate the physical dimensions represented by a raster image and its pixels-per-inch setting before comparing print previews.
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Vector Paths and Raster Images: Keep the Representation with a Resizing Request
Keep vector geometry and raster samples distinct when requesting a larger or smaller packaging graphic.
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