Statistical Fault Localization (SFL) uses coverage profiles (or "spectra") collected from passing and failing tests, together with statistical metrics, which are typically composed of simple estimators, to identify which elements of a program are most likely to have caused observed failures. Previous SFL research has not thoroughly examined how the effectiveness of SFL metrics is related to the proportion of failures in test suites and related quantities. To address this issue, we studied the Defects4J benchmark suite of programs and test suites and found that if a test suite has very few failures, SFL performs poorly. To better understand this phenomenon, we investigated the precision of some statistical estimators of which SFL metrics are composed, as measured by their coefficients of variation. The precision of an embedded estimator, which depends on the dataset, was found to correlate with the effectiveness of a metric containing it: low precision is associated with poor effectiveness. Boosting precision by adding test cases was found to improve overall SFL effectiveness. We present our findings and discuss their implications for the evaluation and use of SFL metrics.