Confidence Intervals

Confidence intervals provide a range of plausible values for population parameters based on sample data, quantifying the precision of our estimates with explicit uncertainty bounds. Unlike point estimates that suggest deceptive precision, these intervals acknowledge sampling variability and communicate the range within which the true value likely falls. A 95% confidence interval means that if we repeated our sampling process many times, about 95% of the resulting intervals would contain the true population parameter.

This approach shifts focus from binary significance decisions toward estimating effect magnitudes with appropriate uncertainty—helping assess whether effects are not just statistically significant but substantively important. Narrow intervals indicate precise estimates from adequate samples, while wide intervals suggest greater uncertainty that might necessitate larger samples or different measurement approaches. When confidence intervals include values that would be practically insignificant, they warn against overinterpreting statistical significance alone. These intervals also facilitate comparisons across studies by showing where estimates overlap, providing more nuanced information than simple significance testing. In modern data science, confidence intervals are particularly valuable for communicating prediction uncertainty to decision-makers, helping them understand not just what the model predicts but how confident they should be in those predictions based on available data.