Fewer experimental combinations can save trial resources while leaving some effects inseparable.
An estimate may represent combined effects
In a fractional factorial design, the chosen fraction determines an alias structure. Some main effects or interactions cannot be estimated separately from the available combinations. Interpretation then depends on the design and assumptions about the aliased effects. A prominent estimated effect should not automatically be described as a uniquely identified physical cause.
A hypothetical design-review question
A buyer receives a reduced three-factor trial matrix. Before accepting “factor C caused the response” as its conclusion, the buyer asks which interactions are aliased with C in that specific design. If the matrix cannot separate them, the conclusion must retain that limitation.
A subsequent design augmentation may resolve the relevant ambiguity, but its feasibility and purpose should be reviewed before more runs are ordered. This example does not prescribe a matrix, machine treatment combination or HM fault investigation.
Ask for the design identity, not only the results
Keep factor definitions, coded and actual levels, the complete run table, design generator or equivalent identification, alias list and actual execution order. Retain the response method and independent-run identities. Explain which effects were assumed negligible and why.
Link the follow-up experiment to the uncertain decision. If only one aliased combination matters to the machinery choice, targeted confirmation may be more useful than declaring every estimated factor proven. Operational constraints and approved process boundaries remain part of selecting the next design; a mathematically valid combination may still be unsuitable to execute.
Trial discussion worksheet
This blank worksheet is for your own project. It contains no H M machine trial result.
| Review question | Reference or observation to retain |
|---|---|
| Which design fraction was used? | Run table and design identity |
| Which effects are aliased? | Specific alias relationships |
| What decision needs separation? | Targeted confirmation question |
Customer Questions
Does a large estimated effect identify one cause?
Only to the extent the design and assumptions separate that effect.
Are all reduced matrices equivalent?
No. Their alias structures and available comparisons differ.
Can further runs remove an ambiguity?
A suitable design augmentation may help; define the needed comparison first.
Primary References
These references support the technical principles discussed in this guide. The worked examples and review questions are educational.
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