Given the e¤ect that outliers can have on regression and speci…cation testing, a vastly used robusti…cation strategy by practitioners consists in: (i) starting the empirical analysis with an outlier detection procedure to deselect atypical data values; then (ii) continuing the analysis with the selected non-outlying observations. The repercussions of such robustifying procedure on the asymptotic properties of subsequent inferential procedures are, however, underexplored. We study the e¤ects of such a strategy on testing for heteroscedasticity. Speci…cally, using weighted and marked empirical processes of residuals theory, we show that the White test implemented after the outlier detection and removal is asymptotically chi-square if the underlying errors are symmetric. In a simulation study, we show that -depending on the type of outliers -the standard White test can be either severely undersized or oversized, as well as have trivial power. The statistic applied after deselecting outliers has good …nite sample properties under symmetry but can su¤er from size distortions under asymmetric errors. Given these results, we devise an empirical modeling strategy to guide practitioners whose preferred approach is to remove outliers from the sample.