Empirical analyses of ecological changes accompanying nutrient enrichment provide one line of evidence for developing protective nutrient criteria. The purpose of my study was to elucidate 2 important aspects of such empirical analyses: 1) how statistical techniques compare regarding types of relationships fit and benchmark values obtained, and 2) how decisions regarding log-transformation of right-skewed nutrient data affect the results. I used data from Great Lakes coastal wetlands describing a suite of water-quality and biotic responses over a large nutrient gradient to conduct side-by-side evaluations of 5 statistical techniques (logistic regression, cumulative probability analysis, linear regression, piecewise regression, classification and regression tree analysis [CART]). With this somewhat noisy data set, differences in goodness-of-fit among techniques that modeled gradual changes vs ones that identified abrupt transitions were remarkably small, providing little evidence for superiority of one over another. However, differences among techniques in nutrient benchmark values and their ecological interpretation were substantial. Log₁₀-transformation of nutrient data had little effect on residuals but shifted the benchmark values for all techniques except CART. Decisions concerning log-transformation ought to be based on implications for deriving criteria rather than perceived statistical assumptions. Multiple statistical techniques and response relationships provide relevant information and no transformation makes all relationships conform to the same pattern, so no cookbook recipe for analyses can be identified. Professional judgment is needed to convey empirical findings toward eventual criteria values regardless of the technique applied.