Association of Clinical Research Professionals (ACRP) Certified Professional Practice Exam

When applying confidence intervals, what does a narrow CI suggest?

1. High variability in data

2. Low variability in data

3. Outdated data collection methods

4. Unidentified confounding variables

The correct answer is: Low variability in data

A narrow confidence interval (CI) indicates low variability in the data. When a CI is narrow, it reflects that the sample mean is closely clustered around the population mean, suggesting that the data points are relatively consistent and have less dispersion. This consistency allows for a more precise estimate of the population parameter, meaning that we can be more confident that the true value lies within the interval. In contrast, a wider confidence interval suggests that there is greater variability or uncertainty about where the true population parameter lies. The width of the CI is directly influenced by both the sample size and the variability within the data: larger samples generally yield narrower CIs, while data with higher variability leads to wider CIs. Therefore, a narrow CI reinforces the idea that the data used for estimation is stable and reliable, supporting better decision-making based on the results.