Large studies have more statistical power than small studies. This means the results from large studies are less likely to be due to chance than results from small studies.
The power of large numbers
You can see the power of large numbers if you think about flipping a coin. Say you are trying to figure out whether a coin is fixed so that it lands on “heads” more than “tails.” A fair coin would land on heads half the time. So, you want to test whether the coin lands on heads more than half of the time.
If you flip the coin twice and get 2 heads, you don’t have a lot of evidence. It wouldn’t be surprising to flip a fair coin and get 2 heads in a row. With 2 coin flips, you can’t be sure whether you have a fair coin or not. Even 3-4 heads in a row wouldn’t be surprising for a fair coin.
If, however, you flipped the coin 20 times and got mostly heads, you would start to think the coin might be fixed.
With an increasing number of observations, you have more evidence on which to base your conclusions. So, you have more confidence in your conclusions. It’s a similar idea in research.
Example of study size in breast cancer research
Say you’re interested in finding out whether or not alcohol use increases the risk of breast cancer.
If there are only a few cases of breast cancer among the alcohol drinkers and the non-drinkers, you won’t have much confidence making conclusions.
If, however, there are hundreds of breast cancer cases, it’s easier to draw firm conclusions about a link between alcohol and breast cancer. With more evidence, you have more confidence in your findings.
The importance of study design and study quality
Study design (the type of research study) and study quality are also important. For example, a small, well-designed study may be better than a large, poorly-designed study. However, when all else is equal, a larger number of people in a study means the study is better able to answer research questions.
Learn about different types of studies.