The Weibull and K-distributions are shown to be very similar over a range of shape parameters ( for the K-distribution), and the mapping gives a means of converting between the shape parameter of the K-distribution to the shape parameter k for the Weibull distribution. The Weibull distribution is very like a truncated normal distribution when , so that a power transform to the power can transform any Weibull distribution to a good approximation of a truncated normal distribution. The transformation was found to transform K-distributed samples to the normal distribution, where is the maximum value after clipping at the 0.9995 quantile level.