In which situation would you employ a confidence interval?

Using a confidence interval is particularly relevant when aiming to estimate the population mean based on a sample. A confidence interval provides a range of values that is likely to include the true population mean with a certain level of confidence (such as 95% or 99%). This method takes into account both the sample mean and the variability within the sample data, resulting in a more nuanced understanding of where the true mean lies in relation to the sample you have observed.

In practice, if only a sample is available, just calculating the sample mean does not give the full picture of how this sample estimates the broader population. The confidence interval offers a statistical framework to quantify the uncertainty inherent in this estimation process, effectively giving researchers insight into the reliability and precision of their mean estimate.

Other options, while relevant in the context of statistics, do not align with the primary purpose of a confidence interval. For instance, determining variance pertains more to understanding the spread of data rather than estimating an average. Computing the mode focuses on identifying the most frequently occurring value in a dataset, which is a different statistical measure altogether. Similarly, finding the range of data values concerns the difference between the highest and lowest data points, rather than estimating central tendencies. Thus, the choice to use a confidence interval