Intervalestimering

Intervalestimering is a statistical method used to estimate an unknown population parameter. Instead of providing a single point estimate, interval estimation provides a range of values within which the true parameter is likely to lie. This range is called a confidence interval.

A confidence interval is characterized by two values: a lower bound and an upper bound. The confidence level, typically expressed as a percentage (e.g., 95%), indicates the probability that the confidence interval contains the true population parameter if the estimation process were repeated many times. A 95% confidence interval means that if we were to take many samples and construct a confidence interval for each, about 95% of these intervals would contain the true population parameter.

The width of a confidence interval is influenced by several factors, including the sample size, the variability of the data (measured by the standard deviation or variance), and the chosen confidence level. Larger sample sizes generally lead to narrower confidence intervals, providing a more precise estimate. Higher confidence levels result in wider intervals, as a larger range is needed to be more certain of capturing the true parameter.

Intervalestimering is widely used in various fields, such as scientific research, market analysis, and quality control, to draw conclusions about populations based on sample data. It offers a more complete picture than point estimation by acknowledging the uncertainty inherent in statistical inference.