When measuring an effect, such as the impact of different embryo grades on birth rates, it’s important to have a sufficiently large sample size to eliminate the role of chance in the results.
A smaller sample size can exaggerate certain results. For example, flipping a coin 100 times (large sample size) will likely show heads and tails close to a 50/50 split. But if you flip the coin only 10 times (small sample size), you might get heads 7 times and tails 3 times, which is a bigger difference from 50/50. Small sample sizes can create misleadingly high or low rates that don’t accurately represent what would happen in a larger, more representative population.
When a study is “powered” for a particular outcome using a power calculation or power analysis, it means the study was designed with a specific sample size to detect meaningful differences for that outcome. This is especially important for randomized controlled trials (RCTs) or clinical studies.