“Statistical rigor” refers to the strict application of statistical methods and principles to ensure that data analysis is accurate, unbiased, and reproducible. In both research and practice, it means:

Proper study design: Using appropriate sampling methods, controls, and randomization.

Correct statistical tests: Choosing and justifying the right tests based on the data and research question.

Transparency: Clearly reporting methods, assumptions, and limitations.

Reproducibility: Allowing others to repeat the analysis and get the same results.

Avoiding p-hacking: Not manipulating data or selectively reporting results to find significance.

In short, statistical rigor safeguards the validity and reliability of conclusions drawn from data.