The phrase "a small error rate" is correct and usable in written English.
It can be used when discussing the frequency of errors in a process, system, or dataset, typically in contexts like statistics, quality control, or machine learning.
Example: "The new algorithm has been tested extensively and shows a small error rate, making it reliable for practical applications."
Alternatives: "a low error rate" or "a minor error rate".
If the employee verification system had even a small error rate, it could cause big problems for large companies like Wal-Mart, which has 1.3 million employees.
Such a small error rate cannot dramatically affect the results and the consequent conclusions.
We confirmed the validity of our diagnostic coding by reviewing a subset of charts, revealing a small error rate compared to the documented clinical impression and no evidence for bias in misdiagnosis favoring either group.
For a small error rate of, which is found on the majority of sites, only a few variant alleles lead to a Bayes factor small enough to call a variant.
The possible explanation is that when the total interference power is equally distributed among six interferers, the probability that at least one of the interferers is strongly faded is greater in the case of multiple interferers, thus leading to a smaller error rate.
To illustrate MAE value for isolated highway, data is found as 1.51 which means that average error of lane position detection is 1.51 in −50 50 scale for a frame which is a very small error rate.
As shown in this plot, the average distance of the ancestors is close to zero for a sufficiently small error rate; that is, after a sufficiently long time almost the entire population has originated from the fittest genotype – evolutionary optimization is effective.
Furthermore, numerous experiments on public databases demonstrate that CBMCC MIH is quite outstanding for fingerprint indexing since it achieves an extremely small error rate with a fairly low penetration rate.
An experimental evaluation of the Ferret prototype shows that (i) Ferret can refine object locations to only 1% of the reader's coverage region in less than 2 min with small error rate (2.22%); (ii) Ferret can detect nomadic objects with 100% accuracy when the moving distances exceed 20 cm; and (iii) Ferret is robust against different movement patterns of user's mobility.
The 0.3% discrepancy consists of both the very small error rate described in the UK controls and the error rate of the PennCNV algorithm.
Specifically, when the error rate was relatively high late (i.e., small error rate difference relative to early learning trials), LPFC putamen coupling was relatively weak early and relatively strong late.
