A confidence interval (CI) is a statistical tool used to estimate the range within which a population parameter is likely to fall, based on sample data. It is expressed as a percentage, indicating the level of certainty that the true parameter lies within that range. For example, a 95% confidence interval suggests that if you were to take multiple samples, approximately 95% of those intervals would contain the true population parameter.

To calculate a confidence interval, you typically use the formula that incorporates the sample mean, standard deviation, sample size, and a z-score corresponding to the desired confidence level. Common confidence levels are 75%, 95%, and 99%, with 95% being the most frequently used. As the confidence level increases, the interval becomes wider, reflecting greater uncertainty about the exact parameter value ¹³.

Understanding confidence intervals is crucial in data science and statistics, as they provide insight into the reliability of sample estimates and help in making informed decisions based on data.