Margin error
The maximum amount that the sample results are expected to differ from those of the actual population.
Since a Sample is used to represent a Population, the sample’s results are expected to differ from what the result would have been if you had surveyed the entire population. This difference is called the margin of error.
The smaller the margin of error, the closer the results of the sample are to what the result would have been if you had surveyed the entire population.
# Margin of error in marketing
The margin of error is also important in marketing. Let’s use A/B testing as an example. A/B testing (or split testing) tests two variations of the same web page to determine which page is more successful in attracting user traffic and generating revenue. User traffic that gets monetized is known as the conversion rate. A/B testing allows marketers to test emails, ads, and landing pages to find the data behind what is working and what isn’t working. Marketers use the confidence interval (determined by the conversion rate and the margin of error) to understand the results.
For example, suppose you are conducting an A/B test to compare the effectiveness of two different email subject lines to entice people to open the email. You find that subject line A: “Special offer just for you” resulted in a 5% open rate compared to subject line B: “Don’t miss this opportunity” at 3%.
Does that mean subject line A is better than subject line B? It depends on your margin of error. If the margin of error was 2%, then subject line A’s actual open rate or confidence interval is somewhere between 3% and 7%. Since the lower end of the interval overlaps with subject line B’s results at 3%, you can’t conclude that there is a statistically significant difference between subject line A and B. Examining the margin of error is important when making conclusions based on your test results.