The ease with which data can be collected and analyzed via personal computer makes it potentially attractive to "peek" at the data before a target sample size is achieved. This tactic might seem appealing because data collection could be stopped early, which would save valuable resources, if a peek revealed a significant effect. Unfortunately, such data snooping comes with a cost. When the null hypothesis is true, the Type I error rate is inflated, sometimes quite substantially. If the null hypothesis is false, premature significance testing leads to inflated estimates of power and effect size. This program provides simulation results for a wide variety of premature and repeated null hypothesis testing scenarios. It gives researchers the ability to know in advance the consequences of data peeking so that appropriate corrective action can be taken.