What does conducting numerous separate statistical analyses increase the likelihood of?

Conducting numerous separate statistical analyses can lead to an increased risk of false positives due to random variation. When multiple tests are performed, the probability of finding at least one statistically significant result just due to chance increases. This phenomenon is often referred to as the "multiple comparisons problem." Each statistical test carries a chance (typically set at 5% for many studies) of incorrectly rejecting the null hypothesis when it is true (Type I error). Therefore, as the number of tests increases, so does the likelihood of obtaining at least one significant result purely by random chance rather than due to any true effect.

In statistical practice, it is crucial to apply corrections for multiple comparisons, such as the Bonferroni correction, which adjusts the significance threshold when multiple tests are performed. This ensures that the likelihood of false positives is minimized, helping researchers report more reliable and valid findings.