The correct answer is B. The residuals indicate a non-constant variance . ANOVA assumes that residuals are approximately normal and that the variance is reasonably constant across groups. When residual plots show non-constant variance , this signals a violation of model assumptions and often indicates that the data should be transformed before interpreting the ANOVA results. The CSSBB materials note that when data are not normal or do not satisfy assumptions for analyses such as t-tests, ANOVA, and process capability , one appropriate action is to transform the data , often using a logarithmic or other power transformation. The CSSBB source also discusses common transformations such as log, square root, exponential, and reciprocal, with Box-Cox cited as a useful technique for finding an appropriate transformation.

By contrast, completely random residual behavior is generally desirable because it suggests no obvious pattern remains. A completely linear normal probability plot also supports the adequacy of the assumptions. Outliers may require investigation, but they do not automatically imply that transformation is the right response. The clearest indicator here is non-constant variance , so B is the verified best answer.