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An experimental design technique grouping similar units into blocks to reduce variance and confounding.
DESCRIPTION
Blocking partitions experimental units into homogeneous groups so treatment comparisons are made within similar conditions, reducing unexplained variability. It increases statistical power and precision, especially when nuisance factors are present. Common designs include randomized complete block and Latin square designs. Overly granular blocks can reduce degrees of freedom and power, so careful balance is needed. Blocking is widely used in agricultural experiments, clinical trials, and A/B testing.
ALTERNATIVE DEFINITIONS
Wikipedia
In the design of experiments, blocking is the arranging of experimental units in groups (blocks) that are similar to one another.
Highlights similarity within blocks as the central concept.
NIST/SEMATECH e-Handbook
Blocking is used to account for the variation among experimental units that is not of primary interest to the experimenter.
Emphasizes controlling nuisance variability to improve inference.
RELATED TERMS
Random Sample Confounding Factorial Design Variance Analysis Randomized Block Design Latin Square Design Predictor Variable Stratification A/B Testing Treatment Effect
COMPETING TERMS
Complete Randomization
KNOWLEDGE AREAS
Data Science Mathematics Testing & Validation
TARGET AUDIENCE
Data Scientist Data Analyst Information Analyst AI Product Manager Business Analyst
EXTERNAL SOURCES
- Wikipedia - Blocking (statistics)
- NIST/SEMATECH - Blocking purpose and examples
- Penn State STAT 503 - Course notes on blocking designs
TOP QUESTIONS
Why use blocking in experiments?
Blocking reduces variance due to nuisance factors, increasing power to detect treatment effects. It leads to more precise and reliable estimates.
Category: Benefits
How do blocks differ from strata?
Blocking is an experimental design concept for assignment and analysis; stratification often refers to sampling or analysis groupings but is closely related.
Category: Concepts
What is a randomized complete block design?
Each treatment appears once in every block, and treatment assignment within blocks is randomized to control for block effects.
Category: Definition
When can blocking hurt performance?
Using too many small blocks can reduce degrees of freedom, inflating variance of estimates and undermining power.
Category: Mistakes
How do I choose blocking factors?
Select known or suspected nuisance variables that cause variability, such as time, location, or batch effects.
Category: BestPractices
Is blocking useful in A/B testing?
Yes. Blocking by traffic source, time, or user segment can reduce variance and improve sensitivity in online experiments.
Category: Usage
How is blocking analyzed statistically?
Include block effects in ANOVA or mixed models to account for block-to-block variation during inference.
Category: Analysis
Can I combine blocking with factorial designs?
Yes. Factorial treatments can be randomized within blocks to study interactions while controlling nuisance variation.
Category: Design
Does blocking prevent confounding?
It reduces confounding with block-related factors, but careful randomization and design are still required to avoid other confounds.
Category: Risk