Tests based on heteroskedasticity robust standard errors are an important technique in
econometric practice. Choosing the right critical value, however, is not simple at all: Con-
ventional critical values based on asymptotics often lead to severe size distortions; and so
do existing adjustments including the bootstrap. To avoid these issues, we suggest to use
smallest size-controlling critical values, the generic existence of which we prove in this article for the commonly used test statistics. Furthermore, sufficient and often also necessary conditions for their existence are given that are easy to check. Granted their existence, these critical values are the canonical choice: larger critical values result in unnecessary power loss, whereas smaller critical values lead to over-rejections under the null hypothesis, make spurious discoveries more likely, and thus are invalid. We suggest algorithms to numerically determine the proposed critical values and provide implementations in accompanying software. Finally, we numerically study the behavior of the proposed testing procedures, including their power properties.