Even with a perfect 2² combination table, executing from low-low through high-high in standard order can mix condition effects with time drift as equipment warms. Design geometry alone does not complete a good experiment.

Separate standard order from run order

Standard order is a mathematical ordering that makes a design easy to read. Actual run order should be randomized where possible. Randomization prevents unknown noise—time drift, operator learning, equipment temperature, or material changes—from attaching systematically to particular treatments.

Randomization does not remove drift. It distributes drift across conditions, supporting treatment as random error; inspect trends in residuals by run order if time is recorded. If full randomization is impossible because of safety, cleaning, or material constraints, record the constraint and use a suitable design and model such as split-plot. Do not arrange runs arbitrarily and call them randomized.

Replication reapplies the treatment to a new experimental unit

Independent replication reruns the same treatment combination on a new independent unit. It observes natural variation directly, estimates pure error, and permits effect SE and lack-of-fit assessment.

Technical repeats read the same sample multiple times on an instrument. They can improve measurement precision and support QC, but do not increase independent n for biological or process variation.

• measuring each condition in 6 donors: donor n=6
• dispensing one donor sample into 6 wells: donor n=1, technical wells=6
• reading each of 3 independent batches twice: batch n=3

A block makes a fair comparison within a known large noise source

When an experiment spans days and Day 1 versus Day 2 differences are expected, use date as a block. Put treatment conditions to be compared in balanced fashion within each block, randomize within blocks, and include block effects in the model to separate date differences from effects of interest.

Blocking is not free. Some factorial interactions can be confounded with block effects; if one block contains only a specific treatment, treatment and block cannot be separated. Check before design that important treatments can appear in every block.

In-Silico Lab: attach drift to standard and random order

1. At drift=1 per run, inspect whether later standard-order conditions are systematically higher.
2. With the same drift, set randomized=yes and inspect how the connection between condition and time changes.
3. Change the seed to confirm randomization is not one uniquely correct order.
4. Explain why the two rows of each treatment combination must be independent replications.

Blinding and allocation concealment are also execution quality

If assessors know treatment, threshold calls or exclusions can be biased. Where possible, blind sample IDs, prespecify exclusion rules, and record every run failure and rerun. Retaining only the randomization seed while hiding execution deviations is not a reproducible design.

A JMP design table needs three distinct columns

Read Pattern or Standard Order, Run Order, and Block separately, linked to raw-data row IDs. Declaring replicate count in software does not guarantee real independence; software cannot know whether a row is a new batch or a reread of the same sample.

Items for an execution receipt

• design version and candidate model
• actual factor units and permitted ranges
• standard order, randomized run order, and seed
• block definition and within-block randomization
• independent experimental-unit IDs and technical-replicate IDs
• run time, equipment, operator, and material lot
• missing runs, reruns, protocol deviations, and reasons
• time of blind release

Takeaways

• Randomization breaks systematic attachment of treatments to time and order noise.
• Independent replication estimates pure error and effect SE.
• Technical repeats do not increase independent experimental units.
• Blocks compare conditions within known large noise sources.
• Arrange blocks so they do not confound treatment effects.
• Preserve both the design table and actual execution deviations.

The next unit connects executed responses to effects, ANOVA, regression equations, and Profiler while retaining residual and model-hierarchy checks.

Official supplementary resources

• NIST/SEMATECH · DOE Terminology
• NIST/SEMATECH · Blocking of Full Factorial Designs

The run table and Lab in this article are educational synthetic material.