This paper analyzes the power of two L moment and the probability plot correlation coefficient (PPCC) goodness-of-fit tests for the Gumbel distribution and the impact of autocorrelation. The two L moment tests are the kappa test suggested by Hosking et al. (1985) using biased PWM estimators, and the L-Cs test suggested by Chowdhury et al. (1991) using unbiased PWM estimators. The generalized extreme value (GEV) distribution with various values of the shape parameter &kgr; was used as the parent distribution. Results show that the L moment-based tests outperform the PPCC test for independent data, or data with small autocorrelations (&rgr;≤0.4). For high autocorrelation (&rgr;=0.8), all tests are invalid because the type 1 error probability is larger than the target value. An example demonstrates consistency problems with scale and shape parameters estimated using the biased PWM estimators; these cause us to advise against their use and to recommend instead unbiased PWM estimators that employ a sample's order statistics. Overall, this paper provides another endorsement of the use of unbiased L moment estimators for goodness-of-fit tests and distribution selection, as well as a recommendation for parameter estimation. ¿ American Geophysical Union 1995