DSC6008 Chap.5 Randomization, Replication and Blocking
Randomization, Replication and Blocking
Give each principle one job
Randomization uses chance to break systematic links between treatment and lurking influences. Replication applies treatments independently to additional units so experimental variation can be estimated. Blocking groups similar units or run conditions and compares treatments within those groups.
A strong design states how each principle is implemented rather than merely listing the three words.
Write randomization as an operation
A description should identify the eligible units, the permitted treatment counts and the chance mechanism used to choose an assignment or run order. Balanced randomization fixes group sizes but does not promise equal covariates in the realised experiment.
Saving the generated allocation and seed preserves the design evidence and gives a randomization analysis the correct set of possible assignments.
Count genuine replicates physically
A replicate is a new experimental unit that receives a treatment independently. Multiple specimens from one treated batch are subsamples unless each specimen can receive a different assignment.
Repeated instrument readings can improve measurement precision, but they do not reveal between-unit experimental variation. The run table should show which rows share one physical assignment.
Plan around nuisance structure
A useful block is known before treatment and is expected to explain response variation without being the scientific target. Run order can still be randomized within each block.
Each block should contain the treatment comparisons needed for the question. If one treatment occurs only in one block, treatment and block are confounded and their effects cannot be separated.
Preserve restrictions in the analysis
A blocked design estimates treatment contrasts within blocks before combining them.
Permuting labels across blocks or fitting a model that ignores the restriction treats impossible assignments as part of the reference process. Pairing is a special small-block case in which the pair difference becomes the basic analysis value.
Design and analysis must describe the same probability mechanism.
Handle failed runs without rewriting history
Record instrument faults, missed treatments and unusable outcomes beside the original planned run. Silently replacing a failed unit or rerandomizing after seeing responses can change assignment probabilities and selection.
A transparent deviation log lets the analysis distinguish the intended design from what was actually executed and supports a sensitivity check when balance is lost.
State the scope of the protected comparison
Random assignment strengthens internal comparison for the units and conditions included. It does not alone make those units representative of a wider population.
The conclusion should name the treatment contrast, experimental unit and block structure, then give a separate scientific argument for generalization. This keeps validity, precision and scope from collapsing into the vague claim that the experiment was controlled.
What this chapter covers
- 01
Random assignment
- 02
Run order
- 03
Genuine replication
- 04
Pseudoreplication
- 05
Block formation
- 06
Nuisance control
Worked example · free
Block a process experiment by production day
- 2Treat day as a block because conditions may shift between days.
- 2Randomize the four treatment run orders separately within each day.
- 3Count three independent batches per treatment as replication.
Key terms
- Random Assignment
- Allocation of treatments to units by a chance mechanism.
- Genuine Replicate
- A new independent experimental unit receiving a treatment.
- Block
- A group of similar units or conditions used to isolate nuisance variation.
Randomization, Replication and Blocking FAQ
What does randomization protect against?
It prevents treatment assignment from following a systematic pattern tied to known or unknown influences. Randomization does not guarantee perfectly equal groups in one experiment, but it supplies a known assignment mechanism and makes design-based uncertainty calculations defensible.
Why is repeated measurement not replication?
Several readings from one experimental unit share the same treatment assignment and physical history. They can improve the measurement of that unit's response, but they do not reveal how the treatment behaves on another independently assigned unit or estimate between-unit experimental variation.
When should a nuisance variable become a block?
Blocking is useful when the variable is known before assignment, predicts meaningful response variation and forms groups within which treatments can be compared. Very weak or tiny blocks may cost degrees of freedom or make the design difficult without improving precision.
Assessment move
For each proposed design, complete three separate sentences: chance is used to assign or order what, the treatment is independently repeated on what, and units are grouped by what nuisance feature. If one sentence cannot be completed, the corresponding principle has not yet been implemented.
Working through Randomization, Replication and Blocking in DSC6008? Sia is AskSia’s AI Statistics tutor — ask any DSC6008 Randomization, Replication and Blocking question and get a clear, step-by-step explanation grounded in how DSC6008 is taught and assessed. Read this chapter free, then take your hardest questions to Sia.
