'large confidence interval' is a correct and usable phrase in written English.
It is typically used in statistics or research to describe the degree of accuracy and reliability of a given data sample. For example, "The results of the survey had a large confidence interval, suggesting that the findings of the study may not be reliable."
However, a large confidence interval was observed; hence, we could not be confident whether the slope of the dose response curve changed for long-term exposures < 6 μg/m.
The large confidence interval observed in β (between 0.3 and 2.0) resulted from the fact that β had a small effect on the infiltration curve.
The large confidence interval of the nest count estimate is probably due to high variance in nest encounter rates, indicating the need for larger sample size and the substantial effort required to produce reliable results using this method.
Given the large confidence interval for the Harvard study, it seems more likely that the true number is closer to the lower limit in the range, along the lines of previous studies – and not the number that has been so prominently in the news.
The mixture obtained a mean value of 62.88 with a slightly large confidence interval of 14.87.
A large confidence interval implies that large variations in the results should be expected, as in the stochastic case.
Even the OR for Florence, with its large confidence interval due to the small sample size, includes the RCT average.
However, due to large confidence interval for the position of QTLs, utility of reported QTLs and the associated markers has been limited in rice breeding programs.
The relatively low number of samples results in large confidence interval (CI) in particular regarding the comparison of AUC values by ROC analysis.
The quite large confidence interval for DAC with 0.01 as minimum support makes uncertain the comparison with the two smallest forests, with 5 and 10 models respectively.
Figure 8 shows that there is a significantly large confidence interval for (R t)) and (B t)), meaning that these variables have a high level of randomness in their nature.
