🔬 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 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