Final answer:
A confidence level in statistics represents the probability that a confidence interval contains the true population parameter, expressed as a percentage. A 95% confidence level means that 95 out of 100 such intervals from repeated samples are expected to contain the true mean. The choice of confidence level affects the width of the interval and reflects how certain one is about the interval containing the true parameter.
Step-by-step explanation:
The confidence level is a percentage expression representing the probability that the confidence interval contains the true population parameter. For example, if the confidence level is 95 percent, we estimate with 95 percent confidence that the true value of the population mean is within the interval estimate.
The confidence interval is an interval estimate around a sample mean, within which the true population mean is expected to lie. It depends on the desired confidence level, the known information about variability, and the sample size.
In order to capture the true population mean, we need a larger interval, which would result from a smaller sample size or higher variability.