Analyzing data from agricultural pest populations regularly detects that they do not fulfill the theoretical requirements
to implement classical ANOVA. Box-Cox transformations and nonparametric statistical methods are commonly used as
alternatives to solve this problem. In this paper, we describe the results of applying these techniques to data from
Thrips
palmi
Karny sampled in potato (
Solanum tuberosum
L.) plantations. The Χ
2 test was used for the goodness-of-fit of negative
binomial distribution and as a test of independence to investigate the relationship between plant strata and insect stages.
Seven data transformations were also applied to meet the requirements of classical ANOVA, which failed to eliminate the
relationship between mean and variance. Given this negative result, comparisons between insect population densities were
made using the nonparametric Kruskal-Wallis ANOVA test. Results from this analysis allowed selecting the insect larval
stage and plant middle stratum as keys to design pest sampling plans.
