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A margin of error is based on the laws of probability. It is the measure in percentage points of the known difference between the statistic (our sample result) and the parameter (meaning our entire population). It is thus the difference between what we have measured and the reality. The basic principle is rather simple: the greater the sample, the smaller the margin of error. However, there’s no use having disproportionately large samples. Most importantly, the sample must be representative.
For example, let’s say that our sample is the number of times we flip a coin to see if we get heads or tails. We know we have a 50-50 chance of flipping heads or tails. If you flip the coin 10 times, you may get 7 heads and 3 tails at random since your margin of error is large. If you flip the coin 100 times, your results will naturally get closer to 50-50, since the margin of error decreases as the sample becomes larger. But past 2000 flips, the decrease in the margin of error is so slight that there is no need to continue flipping.
In short, the margin of error is an indicator of the accuracy of our results. If, for example, there is a margin of error of 2% and a sample result of 30%, it means that the results might differ from the initial results by 2%, and the result may therefore fall between 28% and 32%.
Stephanie Lander is a Research Director at Leger. She joined the Healthcare Insights team after graduating from a Research Analyst post-graduate program in 2016. Stephanie manages both quantitative and qualitative projects for a variety of therapeutic areas and pharmaceutical/healthcare products. She regularly conducts research with healthcare professionals, patients, and consumers.
