The Claim
Small sample sizes in scientific studies increase the likelihood of obtaining results that do not accurately represent true population-level effects due to random sampling variability.
What the research says
Not yet evaluated
We are still looking at what the research says.
These are independent scores, not a percentage. Higher-grade studies count more, so a single strong opposing study can outweigh several weaker ones.
When a study doesn't include enough people, the results might just be due to chance and not reflect what's really going on for most people.
See the scientific wording
Small sample sizes in scientific studies increase the risk of obtaining results that do not reflect true population-level effects due to random sampling variability.
What the research says
1 studyStudy: Machine learning algorithm validation with a limited sample size
The study shows that when scientists use too few people in their studies, especially with complex data, the results can look better than they really are, which supports the idea that small studies are less reliable.
Related videos
Score breakdown, mechanism chain, raw evidence, ideal studies needed & 1 supporting studies
Not medical advice. For informational purposes only. Always consult a qualified healthcare professional before making health decisions.