Statistical buffet is a critical term used to describe a study that includes an excessive number of statistical tests, subgroup analyses, or P-values—often without:

• Clear primary endpoints,
• Correction for multiple comparisons,
• Clinical coherence or actionable insights.

It reflects an approach where data is presented like a buffet: plenty of options, but no structured narrative or scientific digestion.

Warning signs of a statistical buffet:

• Dozens of unadjusted P-values,
• Predictive factors mined from post-hoc analysis,
• Lack of discussion on effect size or clinical relevance,
• Tables overloaded with numbers, but no patient-centered meaning.

It’s the academic equivalent of throwing data at the wall and seeing what sticks.