A confidence interval quantifies the uncertainty in a statistical estimate by defining a range within which the true population parameter is expected to fall, given a specified confidence level, typically 90%, 95%, or 99%. In quantitative research, confidence intervals are reported alongside point estimates such as means or proportions to communicate the precision of findings. A narrower confidence interval signals a more precise estimate, usually the result of a larger or better-designed sample. For enterprise insights teams, understanding confidence intervals is essential when interpreting survey results, brand tracker data, or any quantitative measure used to inform business decisions, because a wide interval may indicate that the sample is too small to support a confident conclusion.