Survey Accuracy Survey accuracy is the extent to which a survey result represents the attribute being measured in the group of interest or population. Determining how accurate the data captured by a survey reflects the entire population requires computing the confidence interval and the confidence level.The confidence interval (Also know as the “Margin of Error” or simply "Error") is usually expressed as a plus or minus percentage, e.g., “+/- 5%”, which indicates that the survey mean score likely deviates from the population mean for that attribute by less than 5%. For example, if 30% of the respondents pick a certain choice on the survey and you have a margin of error of 5%, you can be "sure" that between 25% (30% minus 5%) and 35% (30% plus 5%) of the entire population would pick the same answer.How sure can you be? This is what the confidence level (Also known as "Confidence") tells you. It’s listed as a percentage and tells you how certain you can be of your results.
Survey accuracy standards:For example, a confidence level of 95% means that if you conducted your survey 100 times, you would come up with the same results 95% of the time. Most survey research uses a 95% confidence level since this strikes an optimal balance between accuracy and cost. Which is why we seldom hear the confidence level presented. In some circumstances, we might use a higher confidence level (say, 99%) or a lower one (say, 90%).You should include key demographic questions in each survey so you can see if the survey sample correlates to your membership. If 75% of your members are from the West, for example, responses to a general membership survey should also be 75% from the West. Some types of response biases can be statistically corrected; others may require you to conduct a second mailing to make sure the survey respondents accurately reflect the membership composition.