Overestimation

Overestimation refers to the act of assigning greater value, effect, importance, or certainty to a finding or intervention than is justified by the available evidence.

• Reporting results with exaggerated effect sizes
• Presenting statistically significant but clinically trivial findings as meaningful
• Ignoring confidence intervals, sample limitations, or study design flaws
• Assuming an intervention is more effective than it truly is due to bias or methodological error

• Small sample sizes (→ inflated effect estimates)
• Selective reporting or publication bias
• Lack of blinding or randomization
• Inappropriate statistical methods (e.g., p-hacking)

• A pilot study claims “dramatic symptom reduction” based on a 10-patient sample
• Overinterpreting early subgroup analyses or interim data
• Drawing strong conclusions from observational associations without controlling for confounders

• Leads to misinformed clinical decisions
• May result in harmful overuse of unproven treatments
• Fuels false expectations among clinicians, patients, and policymakers

• Effect Size
• Sample Size Fallacy
• Rhetorical Inflation
• Scientific Spin
• Overgeneralization

• Critical Reading in Medical Literature
• Statistical vs. Clinical Significance