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
Cao J, Zhang S. Multiple Comparison Procedures. JAMA. 2014;312(5):543–544. doi:10.1001/jama.2014.9440
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