Konfidenssitasot

Konfidenssitasot, known in English as confidence intervals, are a fundamental concept in statistical inference. They provide a range of values that is likely to contain an unknown population parameter, such as the population mean or proportion, with a certain level of confidence.

A confidence interval is constructed from sample data and has two main components: the point estimate and the margin of error. The point estimate is a single value calculated from the sample that serves as the best guess for the population parameter. The margin of error quantifies the uncertainty associated with the point estimate, reflecting the variability inherent in sampling.

The confidence level, typically expressed as a percentage (e.g., 90%, 95%, 99%), indicates the long-run proportion of intervals that would contain the true population parameter if the same sampling procedure were repeated many times. For instance, a 95% confidence interval means that if we were to take numerous samples and construct a confidence interval for each, approximately 95% of those intervals would capture the true population parameter.

It's important to understand that a confidence interval does not state the probability that the true parameter lies within a *specific* interval. Instead, it refers to the reliability of the *method* used to construct the interval. Wider confidence intervals indicate greater uncertainty, while narrower intervals suggest more precision. Factors like sample size and variability in the data influence the width of the confidence interval.