What do confidence intervals represent in statistical analysis?

Confidence intervals provide a range of values that are used to estimate an unknown population parameter, such as a population mean. They reflect the degree of uncertainty associated with a sample statistic, helping researchers understand how much confidence they can have in their estimate. Specifically, a confidence interval indicates that there is a specified probability (commonly 95% or 99%) that the population mean falls within the calculated range. This means that if the same sampling process were repeated many times, a significant proportion of the intervals formed from those samples would contain the true population mean.

In contrast, variability of individual scores refers to how much variation there is among individual data points within a dataset, which is not what confidence intervals indicate. Correlation coefficients, on the other hand, measure the strength and direction of the linear relationship between two variables, rather than estimating population parameters. Finally, population variability encompasses how much the data points in a population differ from the mean, but it does not directly relate to the idea of estimating where the population mean might lie based on sample data.

In summary, confidence intervals are fundamentally about estimating the range within which a population mean is likely to fall, hence making option B the correct choice.