When surveys are based on a sample of the target population rather than the entire population, a certain amount of error is inevitable. One such error is sampling error, which occurs because only a subset of the population is surveyed. This error is quantified as the margin of error, which measures how precise the survey results are. The margin of error represents the range within which the actual results are likely to fall, typically expressed in percentage points (e.g., plus or minus 5%).
Two key factors affect the margin of error: the number of respondents and the confidence level. As the number of respondents increases, the margin of error decreases, improving precision. Confidence level indicates the likelihood that the true population result lies within the margin of error. A 95% confidence level is common, meaning that if the survey were repeated 100 times, 95 times the results would fall within the margin of error. Higher confidence levels (e.g., 98%) result in larger margins of error, while lower confidence levels (e.g., 90%) produce smaller margins of error.
For example, a survey with 400 responses might have a margin of error of plus or minus 5 percentage points at a 95% confidence level. If 80% of respondents rated a service as "excellent" or "good," we could say that 95 times out of 100, between 75% and 85% of respondents would give the same rating if the survey were repeated. This is often summarized as a 95% chance that between 75% and 85% of the entire population would rate the service positively.