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We used two standardmethods(the common percentile and the log-power gaussiantransformation)and two novelmeth-ods (weightedpercentileand smoothed spline interpolation) to estimate the 2.5th and 97.5th percentiles of 1000 sets of data from eight diverse distributional forms, generated by Monte Carlo simulation. For each distributionalform we derived an estimate of optimaiperformance.Althoughnone of the four proposed methods closely approximated the optimalperformance bound,the spline interpolationmethod and the weightedpercentilemethodwere superiorto the two standard methods in accuratelyestimatingpercentiles. Additional Keyphrases: statistics smoothed splines weighted percentiles Monte Carlosimulation For optimal interpretation of a specific test result, knowl-edge of the underlying distribution of test values in bot