What are common misconceptions about p-values in hypothesis testing?

2 Misunderstood Significance

The term "statistical significance" often gets misinterpreted. A low p-value does not mean that your findings have practical significance or that they are important. It merely indicates that the observed effect is unlikely to have occurred by chance alone, according to your statistical model. The real-world importance of the result is a separate consideration that requires domain knowledge and practical judgment.

4 P-Values and Truth

It's a misconception to equate the p-value with the truth of a hypothesis. A low p-value does not prove that the alternative hypothesis is true, nor does a high p-value prove the null hypothesis is true. P-values indicate the consistency of the data with the null hypothesis, not the absolute truth of any hypothesis. They are one piece of the puzzle in the broader context of research and evidence.

5 Replication Misconception

Another misconception is that a significant p-value guarantees replication in future studies. Even if a result is statistically significant, it doesn't ensure that future studies will produce the same findings. Replicability depends on many factors, including study design, sample size, and random variation. A p-value provides information about only one study's data in relation to the null hypothesis.

6 Misuse in Research

Lastly, there's a tendency to misuse p-values in research by engaging in practices like p-hacking—manipulating data or statistical analyses until a desirable p-value is achieved. This undermines the integrity of statistical results and can lead to false conclusions. Proper use of p-values involves pre-planned analyses and transparency about all steps taken, not just those that yield significant results.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?