Interval Estimates
Interval estimates are a statistical method used to provide a range of plausible values for an unknown population parameter, such as a mean or proportion, based on sample data. They quantify the uncertainty in estimation by specifying a confidence interval, which indicates the probability that the true parameter lies within the range. This contrasts with point estimates, which give a single value without accounting for variability.
Developers should learn interval estimates when working with data analysis, A/B testing, or machine learning to make informed decisions under uncertainty, such as estimating user engagement metrics or model performance. They are crucial in fields like data science and business intelligence to communicate reliability and avoid overconfidence in sample-based conclusions, especially in scenarios involving small datasets or noisy measurements.