What is indicated by a confidence level in statistical analysis?

A confidence level in statistical analysis refers to the degree of certainty that a population parameter lies within a specified interval. When researchers calculate a confidence interval, they use the confidence level to express how confident they are that this interval includes the true population parameter. For example, a 95% confidence level implies that if the same sampling procedure were performed many times, approximately 95% of the calculated intervals would contain the population parameter.

Understanding this concept is crucial as it informs researchers about the reliability of their estimates. A higher confidence level means a wider interval, indicating more certainty about capturing the true parameter, while a lower confidence level leads to a narrower interval with less certainty. This concept is foundational in inferential statistics, as it helps in making generalizations from samples to populations.