Small sample sizes can affect results in several ways:
Increased Variability: Smaller samples are more likely to yield results that deviate from the true population values, increasing variability. Reduced Power: A small sample size reduces the study's power to detect a true effect, increasing the risk of Type II errors. Bias: Small samples are more susceptible to biases, such as selection bias and observer bias.
