The phrase "an absolute margin of" is correct and usable in written English.
It can be used when discussing a specific, fixed amount or difference in various contexts, such as finance, statistics, or project management.
Example: "The project was completed with an absolute margin of 10% over the initial budget."
Alternatives: "a fixed margin of" or "a definitive margin of".
We estimated a total sample size of 2,100, assuming a headache prevalence ≥10% and an absolute margin of error of 1.3% with 95% confidence interval[13].
However, if the aim is to investigate a relatively rare condition with an expected prevalence of 1%, an absolute margin of error of 0.6% may be very unsatisfactory, in which case the sample size must be increased considerably.
This will allow us detect a proportion of patients with an AE related to care provided in the ED of 5% to within an absolute margin of error of 0.6% (with 95% CI).
We will aim to recruit 5632 patients over 1 year and this will allow us to detect a proportion of patients with an AE of 5% (to within an absolute margin of error of 0.6%).
Studies reporting percentage agreement (plus under/overestimation) for MRI, US and clinical examination by an absolute margin of error are summarised in Figure 5 (no studies reported these data for mammography).
In contrast to our pooled analysis of MDs showing that MRI has a tendency to slightly overestimate pathologic size, studies describing an absolute margin of error suggested that MRI was equally likely to under- and overestimate the pathologic measurement, highlighting that this method may obscure small measurement biases.
With the exception of one study showing a tendency for overestimation (Rosen et al, 2003), MRI appeared equally likely to overestimate and underestimate pathologic tumour size across all absolute margins of error.
The sample size (N = 2,100) was estimated assuming a headache-type prevalence of ≥10 % and absolute margin of error of 1.3 % with 95%% confidence interval (CI).
With decreasing prevalence the absolute margin of error decreases, but the relative margin of error (ie, margin of error/prevalence) increases.
(left| {fleft( {{mathbf{x}}_{new} } right)} right|) represents the absolute margin of the boosted classifiers where the actual normalized margin is defined as (y_{new} cdot fleft( {{mathbf{x}}_{new} } right)), where (y_{i} in left{ { - 1, + 1} right}).
The letter describes how staff are "frequently operating at the absolute margins of clinical safety".
