What does a confidence interval provide?

A confidence interval provides a range expected to contain the true parameter with a specified probability. This is essential in inferential statistics because it allows researchers to estimate the uncertainty around a sample statistic. Instead of giving a single value as an estimate, a confidence interval expresses the range in which the true population parameter is likely to fall, based on the sample data.

For example, if a researcher calculates a 95% confidence interval for a population mean, it means that if the same sampling procedure were repeated many times, approximately 95% of those intervals would capture the true mean of the population. This use of probability, along with the idea of capturing the true parameter, is crucial for making informed decisions based on sample data.

The other options do not accurately describe what a confidence interval does. For instance, it does not provide a definitive value for a statistic, as that would imply certainty that is not inherent in sampling. Additionally, while it may relate to probabilities, it does not give an estimate of probability itself; instead, the interval encompasses a range. Lastly, a confidence interval is focused on a single dataset rather than comparing multiple datasets, which is a different analytical approach.