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The estimate from a survey is never exactly identical to the actual value, even if all the procedures are done correctly. That is because there are bias and sampling errors.

Bias - Something is wrong with the way the sampling was done or the measurements taken.

Sampling error - Just by chance, even in the perfect survey, a sample selected randomly from a population will almost never be exactly the same as the entire population.

Some of the difference between the estimate and the true values is made of bias and some is made of sampling error.

Bias is the difference between survey result and true values due to incorrect measurements being taken (measurement bias) or measurements being taken on a non-representative sample (sampling bias).  Bias cannot usually be quantitatively measured or calculated.

Sampling error is the difference between the survey result and true values due to the random selection of the samples. Sampling error can be predicted, calculated, and accounted for.  There are several measures of sampling error:

• Confidence intervals

• Standard error

• Coefficient of variance

• P values

• Others