Repeatability assesses precision under identical conditions, while reproducibility assesses precision when one or more conditions change, such as the operator, instrument, or location. Reproducibility variance is always greater than or equal to repeatability variance. This distinction is crucial for understanding measurement variability, especially in inter-laboratory comparisons where conditions are inherently different.
Repeatability vs. Reproducibility
The distinction between repeatability and reproducibility is fundamental to Measurement Systems Analysis (MSA) and is formally defined in standards like ISO 5725. Repeatability represents the best-case precision of a measurement system, capturing only the random error inherent in the process under controlled conditions. Reproducibility, on the other hand, introduces systematic and random errors arising from changes in the measurement environment. These changes can include different operators, different setups of the same instrument type, different laboratories, and measurements taken over longer time periods.
The relationship between them is often expressed through variance components. The total variance observed under reproducibility conditions ([latex]s_R^2[/latex]) can be modeled as the sum of the repeatability variance ([latex]s_r^2[/latex]) and the variance between the changing conditions, such as the between-laboratory variance ([latex]s_L^2[/latex]): [latex]s_R^2 = s_r^2 + s_L^2[/latex]. This model highlights that reproducibility will always be worse than or equal to repeatability (i.e., [latex]s_R \ge s_r[/latex]). A método with good repeatability but poor reproducibility is not robust and its results are highly dependent on the specific context, making it unsuitable for standardization. Gage R&R studies are designed specifically to quantify these two components of variation to determine if a measurement system is adequate for its intended purpose, such as controlling a fabricación process.
Tipo
Disruption
Utilización
Precursors
- The concept of experimental design and variance partitioning by Ronald Fisher
- The increasing need for standardized testing methods to support global trade and regulation
- The development of statistical quality control by Shewhart and Deming
- The establishment of proficiency testing schemes by regulatory and accreditation bodies
Aplicaciones
- inter-laboratory proficiency testing
- standardization of test methods across different industries
- evaluating the robustness of a measurement procedure
- collaborative studies for establishing standard reference materials
- Gage R&R studies in manufacturing
Patentes:
Potential Innovations Ideas
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Historical Context
Repeatability vs. Reproducibility
(if date is unknown or not relevant, e.g. "fluid mechanics", a rounded estimation of its notable emergence is provided)
Related Invention, Innovation & Technical Principles