Sample Size Fallacy

The Sample Size Fallacy refers to the misinterpretation or overestimation of the reliability of study findings based on an insufficient or inappropriately small sample size. It occurs when conclusions are drawn with unwarranted confidence despite inadequate statistical power or representativeness.

• Treating results from a small or underpowered study as if they were conclusive
• Failing to account for random variation, outliers, or false positives
• Reporting subgroup differences or dose-response trends in small samples as if statistically robust
• Using limited data to justify broad or clinical recommendations

• Inflated effect sizes due to random variation
• Increased risk of type I (false positive) and type II (false negative) errors
• Misleading interpretations, especially in exploratory or post hoc analyses

• A study with 12 patients per arm concluding that one drug is “more effective”
• Subgroup analysis in a trial with 8 total responders
• Descriptive statistics used to define “optimal dosing” without statistical significance

• Undermines scientific validity
• Leads to publication of spurious results
• Wastes resources on follow-up studies of false leads

• Underpowered Study
• Publication Bias
• Overgeneralization
• Rhetorical Inflation
• Statistical Significance vs. Clinical Relevance

• How to identify flaws in scientific articles
• Effect Size Interpretation