Survey Sampling Error

In most research projects, the sample size is determined by the level of acceptable error. Errors in survey research arise from three distinct sources:

1. Missing data from non-interviews and from item non-response.
2. Inaccurate information given by respondents or recorded by interviewers.
3. Sampling error.

While the first two sources of error are independent of the sample size, the third source of error is directly correlated with it.

Though interviewing all the individuals in a target population would eliminate sampling error, practical considerations such as cost prohibit such precision. Therefore all surveys that draw a sample of people from the total population are subject to sampling error. But how much sampling error is acceptable?

Here's how it works: If we conduct a survey 100 times, we can be confident that 95 times out of the 100, characteristics of the sample would reflect the characteristics of the targeted population plus or minus the number of percentage points shown in the table below.

For example, if we survey 2500 people from the targeted population, and 55% of those people answer "yes" to a given question, we can be confident that between 53% and 57% of the total population would have answered "yes" had they been asked the same question.

Sample Size	Sample Error	Sample Size	Sample Error
25	20.0%	800	3.5%
50	14.2%	1000	3.2%
100	10.0%	1200	2.9%
150	8.2%	1500	2.6%
200	7.1%	2000	2.2%
250	6.4%	2500	2.0%
300	5.8%	3000	1.8%
400	5.0%	4000	1.6%
500	4.5%	5000	1.4%
600	4.1%