The phrase "accuracy confidence interval" is correct and usable in written English.
It can be used in statistical contexts to refer to a range of values that is likely to contain the true value of a parameter, with a specified level of confidence.
Example: "The accuracy confidence interval for the survey results indicates that we can be 95% confident that the true population mean lies within this range."
Alternatives: "precision confidence interval" or "reliability confidence interval".
However, sampling 400 representative members out of a population of 9,000 statistically yields a result with good accuracy (confidence interval better than 5%).
Based on prevalence of depression at 20% from other studies [ 17, 23, 24] with ± 5% accuracy, confidence interval of 95%, power of.8, a sample size of 482 was estimated using Piface software, 2004 version.
The proposed approach is applied to two realistic reliability assessment problems, revealing their applicability and accuracy in confidence interval estimation of reliability indices.
For more accuracy, 95%% confidence intervals based on the Bootstrap method was used [14 16].
Table 1 shows statistics of the detection at the BOP, namely the TF, FP, accuracy, and confidence intervals of a proportion at a significance level α = 0.05.
A substantial literature exists on the estimation of k, but most attention has focused on datasets that are not highly overdispersed (i.e., those with k≥1), and the accuracy of confidence intervals estimated for k is typically not explored.
For the measures of diagnostic accuracy 95% confidence intervals are reported.
The sensitivity, specificity and accuracy with confidence intervals were also calculated to compare the findings of the two scanning techniques.
Contingency tables were used to determine sensitivity, specificity and overall accuracy (95% confidence intervals) of the Kudo classification by NBI and of the AFI colour.
This sample size was selected because it would provide sufficient power and sensitivity for each of the planned analyses (ie, provide planned accuracy for confidence intervals for concurrent validity and provide power for effect-size differences in known groups validity).
Preacher and colleagues have developed a bootstrapping strategy for estimating conditional indirect effects which makes no assumption about the shape of the distribution and incorporates bias correction and acceleration that improve the accuracy of confidence intervals.
