Hedging effectiveness is the proportion of price risk removed through hedging. Empirical hedging studies typically estimate a set of risk minimizing hedge ratios, estimate the hedging effectiveness statistic, apply the estimated hedge ratios to a second group of data, and examine the robustness of the hedging strategy by comparing the hedging effectiveness for this "out-of-sample" period to the "in-sample" period. This study focuses on the statistical properties of the in-sample and out-of-sample hedging effectiveness estimators. Through mathematical and simulation analysis we determine the following: (a) the R2 for the hedge ratio regression will generally overstate the amount of price risk reduction that can be achieved by hedging, (b) the properly computed hedging effectiveness in the hedge ratio regression will also generally overstate the true amount of risk reduction that can be achieved, (c) hedging effectiveness estimated in the out-of-sample period will generally understate the true amount of risk reduction that can be achieved, (d) for equal numbers of observations, the overstatement in (b) is less that the understatement in (c), (e) both errors decline as more observations are used, and (f) the most accurate approach is to use all of the available data to estimate the hedge ratio and effectiveness and to not hold any data back for hedge strategy validation. If structural change in the hedge ratio model is suspected, tests for parameter equality have a better statistical foundation that do tests of hedging effectiveness equality.