Confidence Intervals
Confidence intervals are a statistical concept used to estimate the range of values within which a population parameter (e.g., mean, proportion) is likely to fall, based on sample data. They provide a measure of uncertainty around an estimate, typically expressed with a confidence level (e.g., 95%) that indicates the probability the interval contains the true parameter. This is fundamental in inferential statistics for making predictions and decisions from limited data.
Developers should learn confidence intervals when working with data analysis, A/B testing, machine learning model evaluation, or any scenario requiring statistical inference from samples. For example, in software development, they are used to estimate user engagement metrics, error rates in systems, or performance improvements from experiments, helping to quantify reliability and avoid overinterpreting noisy data.