Interval Estimation
Interval estimation is a statistical method used to estimate an unknown population parameter (such as a mean or proportion) by providing a range of plausible values, called a confidence interval, based on sample data. It quantifies the uncertainty associated with the estimate, typically expressed with a confidence level (e.g., 95%) that indicates the probability the interval contains the true parameter. This contrasts with point estimation, which gives a single value without a measure of reliability.
Developers should learn interval estimation when working with data analysis, machine learning, or A/B testing to make informed decisions under uncertainty, such as estimating user engagement metrics or model performance with confidence bounds. It is essential in fields like data science, business intelligence, and research to avoid over-reliance on point estimates and to communicate statistical findings effectively, ensuring robust conclusions from limited data.