Statistical evidence is vital, but the term "statistical significance" often leads to confusion about its true meaning. To clarify, we should be using more precise language that better reflects the nature of statistical findings.

Updates

• 2024–08–10: Çetinkaya-Rundel and Hardin have released "Introduction to Modern Statistics V2." They advocate for replacing "statistically significant" with "statistically discernible" to mitigate misunderstandings.
• 2023–08–11: Professor Jeffrey Witmer published an editorial promoting "statistical discernibility" over "statistical significance" in the Journal of Statistics Education.

It's essential to understand the distinction between the significance of evidence and the practical significance of findings.

Key Points

• UPDATE (2023–09–15): The amount of evidence required to discern a small or trivial finding is greater than that needed for larger, meaningful results. The same p-value can indicate both scenarios, yet only one carries true significance.
• UPDATE (2023–09–13): Consider discernibility as related to 1 minus the p-value or its reciprocal; this can help in exploratory studies to highlight findings with substantial statistical evidence, irrespective of their importance.

1. A "significant" p-value does not equate to a "significant" finding; it only reflects the quality of statistical evidence for the true value.
2. Conversely, a "discernible" finding indicates that the statistical evidence effectively distinguishes the true value.

Statistical evidence itself is not the issue; rather, it is the misinterpretation of "statistical significance" as a definitive marker of scientific importance that leads to misconceptions.

Writing Template

The following is a model you may adapt for your own work. Customize the text for clarity on your stance and to avoid plagiarism.

• When reporting a variable, use "discerning" instead of "significant," or refer to its "discernibility" rather than its "significance."

> We will deviate from traditional statistical terminology in two significant ways. Firstly, we will avoid the term "statistically significant" to prevent its conflation with "significant," which often implies a scientific or clinical importance unrelated to the statistical quality of the evidence. This confusion is a known issue that has contributed to challenges in replication within biomedical and psychological research.

> Secondly, we will report p-values plainly, avoiding a binary classification of "statistically significant" versus "not significant," as this diminishes the nuanced information that p-values provide.

We aim to replace ambiguous language with clearer alternatives like "statistically discernible," "statistically evident," and "statistically reliable."

Real-World Applications

Publications that have successfully adopted this terminology include:

• Çetinkaya-Rundel M, Hardin J. Introduction to Modern Statistics V2. OpenIntro; 2021.
• Daza EJ, Wac K, Oppezzo M. "Effects of Sleep Deprivation on Blood Glucose, Food Cravings, and Affect in a Non-Diabetic: An N-of-1 Randomized Pilot Study." Healthcare 2020.
• Matias I, Daza EJ, Wac K. "What Possibly Affects Nighttime Heart Rate? Conclusions from N-of-1 Observational Data." Digital Health 2022.

Why This Matters

The ongoing misuse of "statistical significance" in scientific discourse reflects deeper issues in communication within the field. Addressing these challenges requires collective effort and a willingness to adapt.

Conclusion

We must continue to critically evaluate the statistical quality of our evidence while avoiding the pitfalls of conflating statistical quality with scientific merit. Let’s strive for clearer communication in our research.

References

• Amrhein, V., Greenland, S., & McShane, B. "Scientists Rise Up Against Statistical Significance." Nature, 2019.
• Barnett, M.L., & Mathisen, A. "Tyranny of the p-value." Journal of Dental Research, 1997.
• Additional references as listed in the original article.

Eric J. Daza is a data science statistician with extensive experience in clinical research and public health. For more insights, visit [ericjdaza.com](http://ericjdaza.com).