Short answer: sample size is important because it affects how precisely a study can estimate an effect and how likely it is to detect a real difference if one exists.
A study based on ten people may miss a real effect because there is too much random variation. It may also produce an exaggerated result by chance. Larger samples usually give more stable estimates, but sample size must be understood alongside study design, measurement quality, and the size of the effect being studied.
Power and precision
Statistical power is the probability that a study will detect an effect of a given size if the effect is really there. Low-powered studies are more likely to miss true effects. They can also contribute to unreliable literature because the significant results that do appear may be unusually large or unstable.
Sample size also affects confidence intervals. A larger sample usually narrows uncertainty around an estimate. That can help readers see whether a result is precise enough to matter in practice.
Why bigger is not always enough
A huge sample cannot fix a badly biased study. If the wrong outcome is measured, if the sample is unrepresentative, or if the design fails to address confounding, more data may simply make a wrong answer look more precise. Quality matters as much as quantity.
Researchers should ideally justify sample size before collecting data. They should explain the expected effect size, acceptable error rate, and practical constraints. Readers should be cautious when a study makes large claims from a small or poorly explained sample.