Controlling for Multiple Tests.

Area of Science:

• Statistics
• Research Methodology

Background:

• The P-value quantifies the probability of observed results occurring by chance under the null hypothesis.
• A conventional P-value threshold of <0.05 is widely used to determine statistical significance.
• Performing multiple statistical analyses increases the risk of false positive results.

Purpose of the Study:

• To highlight the issue of false positives arising from multiple statistical tests.
• To advocate for the use of advanced statistical techniques in research.

Main Methods:

• Discussion of the implications of the P-value threshold in the context of multiple comparisons.
• Conceptual explanation of how false positives accumulate with an increasing number of tests.

Main Results:

• With 20 independent statistical tests, one false positive is expected at a P-value threshold of <0.05, assuming the null hypothesis is true for all.
• The probability of encountering at least one false positive increases significantly with the number of tests conducted.

Conclusions:

• The conventional P-value threshold is insufficient to control for false positives in studies with multiple statistical analyses.
• Advanced statistical methods are necessary to mitigate the risk of false positives and ensure the validity of research findings.