Last week a colleague called me out saying we need to mind the gap — guess what she meant — It was the concept of Margin of Error.

Errors from choices of sample, non-response/data capture, among others, are errors faced daily by analyst. This random sampling errors account for a percentage to how confident you can be in the results of a study/analysis. This percentage is called the Margin of Error or Confidence Interval — often defined as statistical range in which an estimate may vary due to random sampling error.

For example, in a survey of 500 people, if 60% of respondents prefer product A over product B, the true percentage of the entire population who prefer product A may be different due to random sampling error. If upon closer examination, 10 respondents were found to be missing their preference data, this would introduce a margin of error of ±2% (2% being the percentage of the total sample size represented by the 10 missing responses). Thus, the actual percentage of people who prefer product A could range from 58% to 62%.

Understanding and capturing margin of error is important because, it allows one to interpret the results of a study more accurately. It helps you to determine how confident decision makers should be in the findings and whether they’re statistically significant. In addition, it can also help you to design studies/data collection that are more accurate and reliable by ensuring that you have an appropriate sample size and level of confidence.

It’s also important to note that margin of error may also help remove bias in data analysis and improves interpretation of the results of a study more accurately. By calculating and reporting the margin of error, analyst can provide a measure of the precision of their estimates and enable others to evaluate the results accordingly.