A small sample size refers to:
A study population that is too limited in number to provide reliable, generalizable, or statistically robust conclusions.
🔍 Key consequences of small sample size:
🔸 Low statistical power – increased risk of type II error (false negatives)
🔸 Inflated effect sizes – due to random variation or outliers
🔸 Wide confidence intervals – low precision in estimating effect
🔸 Limited subgroup analysis – cannot control for confounders
🔸 Greater impact of missing data – one dropout can skew results
🔸 Reduced external validity – findings may not apply to broader populations
⚠️ Clinical interpretation:
Even if a small study finds statistically significant results, they may be:
Unstable across repeated samples
Not replicable in larger trials
Misleading if underpowered and selective in reporting