What misconceptions might you have about interpreting p-values?

1 P-value Basics

A p-value is not the probability that the null hypothesis is true. This is a common misconception; the p-value actually measures the probability of observing data at least as extreme as the data observed, assuming the null hypothesis is true. It is a subtle but critical distinction. A low p-value indicates that under the null hypothesis, the observed data would be unlikely, which may suggest that the null hypothesis is false. However, it does not provide a direct probability of the null hypothesis itself being true or false.

2 Threshold Misuse

The 0.05 threshold for p-values is often misinterpreted as a magical line that separates true findings from false ones. This threshold is arbitrary and does not guarantee that results are valid or invalid. A p-value slightly less than 0.05 is not necessarily strong evidence against the null hypothesis, nor is a p-value slightly above 0.05 proof that there is no effect. The 0.05 threshold should be used as a guideline rather than a definitive cut-off, and results should be considered within the context of the entire study.

3 Effect Size

Overlooking the effect size is a common pitfall when interpreting p-values. A statistically significant result (typically, a p-value below 0.05) does not necessarily imply that the effect is practically significant or large. It's important to consider the effect size, which measures the magnitude of the difference or association, alongside the p-value. A small effect size might not be meaningful, even if it's statistically significant, especially in studies with large sample sizes where small differences can appear statistically significant.

4 P-hacking Concerns

P-hacking refers to the practice of manipulating data analysis until one finds a desirable p-value. This can involve selectively reporting results, cherry-picking data, or repeatedly testing hypotheses until you get a p-value below 0.05. P-hacking undermines the integrity of statistical results and can lead to false positives—concluding that there is an effect when there isn't one. Being aware of this practice and ensuring transparency in your analysis can help maintain the credibility of your findings.

5 Reproducibility Issues

Reproducibility is a hallmark of sound science, but many overlook that a single p-value does not ensure reproducibility. A p-value obtained from one study is specific to that study's data and methodology. Even with a low p-value, there's no guarantee that an independent study will yield similar results. Reproducibility requires consistent findings across multiple studies, and relying solely on p-values for reproducibility assessments can be misleading.

6 Bayesian Perspective

Finally, it's worth considering a Bayesian perspective on p-values, which incorporates prior knowledge or beliefs into the analysis. Bayesian statistics offer an alternative to p-values by providing the posterior probability of a hypothesis given the data. This approach can be more intuitive as it allows for direct probability statements about hypotheses. While p-values remain widely used, understanding Bayesian methods can provide a fuller picture of statistical evidence.

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?