Template

DOE & Advanced Problem-Solving: Experiment Template

Interactive experiment planning and data-capture template for small factorial DOE. Includes objective and power target, factors and levels, randomized run plan, data capture area, ANOVA-focused analysis checklist, interpretation guidance, and a worked 2x2 example to illustrate common decisions.

Interactive Tool

DOE & Advanced Problem-Solving: Experiment Template

Plan small factorial experiments that answer practical causal questions

This interactive template helps you design, document, run, and interpret small factorial experiments (2-level and simple multi-level designs). It focuses on clear objectives, minimum detectable effect / power targets, careful factor/level choice, randomized run order, disciplined data collection, and an ANOVA-oriented checklist so results are actionable rather than confusing.

Fill the form to capture an experiment plan, paste your run-order and collected data as CSV, and save the record. An example 2x2 experiment is shown below to make decisions concrete.

Worked 2x2 example

Objective: Reduce cycle time of operation X by changing tool setting and operator preload procedure.
Hypothesis: Changing tool setting to A2 and using preload procedure P reduces cycle time by at least 10% compared with baseline.
Factors: ToolSetting (A1, A2), PreloadProcedure (P1, P2).
Response: Cycle time (seconds), measured with stopwatch to nearest 0.1s.
Design: Full 2x2 factorial, 3 replicates per cell (total 12 runs), randomized run order within blocks of 4 runs to manage operator shift.
Analysis checklist: verify randomization and measurement consistency, run ANOVA (two-way), inspect main effects and interaction, plot means with error bars, estimate effect sizes and 95% CI, confirm whether the observed reduction meets the practical threshold (10%) before recommending process change.

Describe the operational outcome you want to improve and why it matters. Be specific and measurable.
State the expected direction and approximate size of effect (e.g., 'Setting A2 decreases mean cycle time by ≥10%').
Name the metric you'll measure (e.g., cycle time, defect rate, tensile strength).
Units for the response variable (e.g., seconds, percent, mm).
How will you measure? Include instruments, resolution, sampling rules, and who will measure.
Type I error threshold (commonly 0.05).
Desired power (probability of detecting the target effect). Typical target: 0.8 or 0.9. Use power to size your experiment when practical.
The smallest change that would be meaningful to the business (e.g., 10% reduction in cycle time). Express as absolute or relative amount.
Select how many factors you plan to test (1–4). If you need more, consider blocking or sequential experimentation.
E.g., ToolSetting.
List levels separated by commas (e.g., A1, A2). For numeric factors you may write numeric values.
Only required if you selected 2 or more factors.
List levels separated by commas.
Optional.
Optional.
Optional.
Optional.
Choose a simple design appropriate for small factorials. Fractional designs reduce runs but complicate interpretation.
E.g., shift, operator, machine. Use blocking to control known sources of variation.
Number of repeated observations per combination. More replicates increase power and allow error estimation.
Automatically equals combinations × replicates. Enter or adjust if needed.
Describe how you will randomize runs (e.g., randomize within blocks, seed used, constraints).
Paste a small CSV with header. Example header: Run,Factor1,Factor2,Block,Replicate. This is your execution plan; keep it with the experiment record.
Explain data capture columns (e.g., Run,Factor1,Factor2,Replicate,Response,MeasureTimestamp,Operator). Recommend consistent file naming and backup.
After running the experiment, paste collected values in CSV format with header. The system stores the CSV for later analysis export. Example header: Run,Factor1,Factor2,Replicate,Response.
Check items to confirm before interpreting results.
Summarize what the experiment shows in plain language, practical implications, limitations, and next steps.
How to export plan, run-order, and collected data for local analysis or regulatory records.
Checking this confirms you have reviewed the basic guidance; it does not replace statistical consultation if needed.
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