====== 🔬 Small Sample Size ======

A small [[sample size]] refers to:

A study [[population]] that is too limited in number to provide reliable, generalizable, or statistically robust [[conclusion]]s.

🔍 Key consequences of small sample size:

🔸 Low statistical power – increased risk of type II error ([[false negative]]s)

🔸 Inflated effect sizes – due to random variation or outliers

🔸 Wide [[confidence interval]]s – 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