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Two thousand seven hundred and forty‐seven isolates of Sclerotinia sclerotiorum were sampled from four field populations of canola in western Canada. Each field was sampled in a grid of 128 50‐m 50‐m quadrats plus four intensive quadrats each sampled in a diagonal transect. Sampling was done at two phases of the disease cycle: (1) from ascospore inoculum on petals and (2) from disease lesions in stems. A total of 594 unique genotypes was identified by DNA fingerprinting. In each field, a small group of clones represented the majority of the sample, with a large group of clones or genotypes sampled once or twice. Clone frequencies were compared by χ2 tests. The difference in profiles of clone frequencies for the two fields sampled in 1991 was not significant, but in 1992 the difference in profiles was marginally significant, indicating some local population substructure. The difference in profiles of clone frequencies for petals and lesions was not significant in each of the two fields sampled in 1991. In each of the two fields sampled in 1992, however, the difference was highly significant, consistent either with selection for some clones or with waves of immigration during the disease cycle. Nine of the 30 most frequently sampled clones from this study were previously recovered in a macrogeographical sample from western Canada in 1990. For spatial analyses, randomization tests indicated no significant spatial aggregation of either clones on petals or clones from lesions. Also, isolates of a clone on petals were not closer to isolates of the same clone from lesions than could be predicted by chance. Both observations suggest spatial mixing of ascospore inoculum from resident or immigrant sources.

This paper demonstrates an inflation of the type I error rate that occurs when testing the statistical significance of a continuous risk factor after adjusting for a correlated continuous confounding variable that has been divided into a categorical variable. We used Monte Carlo simulation methods to assess the inflation of the type I error rate when testing the statistical significance of a risk factor after adjusting for a continuous confounding variable that has been divided into categories. We found that the inflation of the type I error rate increases with increasing sample size, as the correlation between the risk factor and the confounding variable increases, and with a decrease in the number of categories into which the confounder is divided. Even when the confounder is divided in a five-level categorical variable, the inflation of the type I error rate remained high when both the sample size and the correlation between the risk factor and the confounder were high.

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