NonRepeatable

NonRepeatable is an adjective used to describe phenomena, results, or measurements that cannot be reproduced exactly when the experiment or observation is repeated under the same stated conditions. It stands in contrast to repeatable outcomes, which yield consistent results across trials, and to irreproducible results, which fail to be reproduced altogether due to errors or bias.

Causes of nonrepeatability include intrinsic randomness in the system being studied, changing environmental conditions, subtle differences in setup, instrument drift, and measurement noise. In observational sciences, nonrepeatable results may reflect real variability in the phenomenon rather than a flaw in methodology, and they can still be scientifically meaningful when properly interpreted.

Contexts and usage: In laboratory experiments, researchers strive to minimize nonrepeatability through randomization, calibration, and standardized protocols. In computational work, non-deterministic algorithms and stochastic inputs can produce nonrepeatable results unless a fixed random seed or deterministic settings are used. In data management, non-repeatable reads describe a form of inconsistency where a database query returns different results when executed again within the same transaction under concurrent updates.

Impact and interpretation: Nonrepeatability affects confidence in findings, the reproducibility of science, and the reliability of measurements. It is often addressed by transparent reporting of methods, statistical analysis, and replication across independent teams or time periods to determine whether observed variability reflects noise, bias, or genuine change.

See also: repeatability, reproducibility, irreproducible results, non-deterministic, non-repeatable read.