Code-coverage-based test data adequacy criteria typically treat all coverable code elements (such as statements, basic blocks, or outcomes of decisions) as equal. In practice, however, the probability that a test case can expose a fault in a code element varies: some faults are more easily revealed than others. Thus, several researchers have suggested that if one could estimate the probability that a fault in a code element will cause a failure, one could use this estimate to determine the number of executions of a code element that are required to achieve a certain level of confidence in that element's correctness. This estimate, in turn, could be used to improve the fault-detection effectiveness of test suites, and help testers distribute testing resources more effectively. This conjecture is intriguing; however, like many such conjectures it has never been directly examined empirically. If empirical evidence were to support this conjecture, it would motivate further research into methodologies for obtaining fault-exposure-potential estimates and incorporating them into test data adequacy criteria. This paper reports the results of experiments conducted to investigate the effects of incorporating an estimate of fault exposure probability into the statement coverage test data adequacy criterion. The results of these experiments, however, ran contrary to the conjectures of previous researchers. Although incorporation of the estimates did produce statistically significant increases in the fault-detection effectiveness of test suites, these increases were quite small, suggesting that the approach might not be able to produce the gains hoped for, and might not be worth the cost of employing.