Graphical interpretation of confidence curves in rankit plots

A wellknown transformation from the bellshaped Gaussian (normal) curve to a straight line in the rankit plot is investigated, and a tool for evaluation of the distribution of reference groups is presented. It is based on the confidence intervals for percentiles of the calculated Gaussian distribution and the percentage of cumulative points exceeding these limits. The process is to rank the reference values and plot the cumulative frequency points in a rankit plot with a logarithmic (ln=log(e)) transformed abscissa. If the distribution is close to lnGaussian the cumulative frequency points will fit to the straight line describing the calculated lnGaussian distribution. The quality of the fit is evaluated by adding confidence intervals (CI) to each point on the line and calculating the percentage of points outside the hyperbolalike CIcurves. The assumption was that the 95% confidence curves for percentiles would show 5% of points outside these limits. However, computer simulations disclosed that approximate 10% of the series would have 5% or more points outside the limits. This is a conservative validation, which is more demanding than the KolmogorovSmirnov test. The graphical presentation, however, makes it easy to disclose deviations from lnGaussianity, and to make other interpretations of the distributions, e.g., comparison to nonGaussian distributions in the same plot, where the cumulative frequency percentage can be read from the ordinate. A long list of examples of lnGaussian distributions of subgroups of reference values from healthy individuals is presented. In addition, distributions of values from welldefined diseased individuals may show up as lnGaussian. It is evident from the examples that the rankit transformation and simple graphical evaluation for nonGaussianity is a useful tool for the description of subgroups.