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In the last years, a project risk analysis has become increasingly important because it has proved to be a valuable tool, e.g., in interpreting project data provided by experimental judgments. Project risk data cannot be answered in a parametric framework easily; moreover, original risk data sizes are too small to estimate the standard deviation of risk data. When parametric modeling and theoretical analysis are difficult, the Jackknife is good alternative to calculate the standard deviation for estimator. Therefore, in this paper, a new approach based on a non-parametric Jackknife technique is proposed to analyze risk data in order to reduce the standard deviation of project risk data. An example of risk data (probability and impact) is used to show the applicability of the proposed technique.