Problems can arise when researchers try to assess the statistical significance of more than 1 test in a study. In a single test, statistical significance is often determined based on an observed effect or finding that is unlikely (<5%) to occur due to chance alone. When more than 1 comparison is made, the chance of falsely detecting a nonexistent effect increases. This is known as the problem of multiple comparisons (MCs), and adjustments can be made in statistical testing to account for this.1