The phrase "aggregation of errors" is correct and usable in written English.
It can be used in contexts related to statistics, data analysis, or any field where multiple errors are combined or considered together.
Example: "The final result was affected by an aggregation of errors from various sources, leading to inaccurate conclusions."
Alternatives: "compilation of errors" or "collection of errors".
The objectives of this study are to describe the prevalence and causes of visual impairment in the population of Tehran city; the prevalence of refractive errors, lens opacity, ocular hypertension, and color blindness in this population, and also the familial aggregation of refractive errors, lens opacity, ocular hypertension, and color blindness within the study sample.
To describe the familial aggregation of refractive errors, lens opacity, ocular hypertension, and color blindness within the study sample.
This is a particularly significant problem in cancer sequencing, as subclonal variation and sample impurity give rise to mutations at the same low allelic fractions as aggregations of systematic error.
Rankings based on comparison alone are insensitive to those correlations, and the normalization of errors makes aggregation possible without going through the complex exercise of modeling the relations between settings.
This effect is commonly referred to as the optimality error (see Francis et al. (2009) for a detailed analysis of errors associated with aggregation in the context of location models).
For this reason, unemployment and inflation figures for NECTAs have been imputed without adjustment to the corresponding CBSAs (hence, at the highest level of aggregation to minimize errors).
Consequently, a new sample is predicted, and the estimation of errors can be calculated at the level of iteration and aggregation [25, 26].
Wojciechowski, R. et al. Heritability of refractive error and familial aggregation of myopia in an elderly American population.
Therefore, using a large database of observations, we documented (A) errors in the assignment of weights to vital sign values, (B) errors in weight aggregation (summing the individual weights for each vital sign), (C) the effect of time of day on the occurrence of errors, and (D) how errors affected the clinical response that should have occurred.
Using the primary datasets reduces potential sources of errors introduced when compiling data from published studies that may have used different methods of data extraction and aggregation.
