How accurate are the survey results?

If you ask this question, you are concerned with the precision of the data collected. In a sample survey, we infer results about the target population by studying a representative subset of the population. While this can help us save resources, no gain comes without a price. Estimates based on the sample selected will generally differ from those of the entire population. This extent of variation between estimates obtained from a sample and the true value is known as the** sampling error**. Take a look at the example below.

Factors influencing the sampling error include the:

sample design (e.g. how the training participants are selected for the survey), sample size (e.g. the number of training participants to be interviewed), estimation method (e.g. do we gross up the survey results to the population using only one or multiple expansion factors?), variability of the population (e.g. does the income of the training participants differ widely?), and characteristics studied (e.g. are there differences among those working in the Northern part of Coconut Island than those in the Southern part).

The reliability of survey estimates are commonly measured by a group of indicators, including the standard error, relative standard error and confidence interval. Learn more here.