Statistics Canada produces data on numerous topics of interest to Canadians. For example, the census of population collects data on every individual to produce very accurate counts every five years. To produce accurate economic and social data on a more frequent and timely basis, Statistics Canada typically conducts surveys that collect data on a random sample of individuals or businesses.

For example, the Monthly Survey of Manufacturing (MSM) publishes the values (in Canadian dollars) of sales of goods manufactured, inventories and orders six weeks after the end of every month. On October 16, 2014, the MSM estimated that $52,100 million of goods manufactured were sold in Canada in August 2014. Statistics Canada produced this estimate on the basis of data collected from a random sample of 10,500 business establishments across Canada.

Like any other survey, the aim of the MSM is to produce the most accurate results possible. How can we determine whether the MSM estimate of $52,100 million in sales of goods manufactured is, in reality, close to the actual level of sales in August 2014 in Canada? To do this, we use two measures of precision—bias and variance.

Variance is relatively easy to measure in a survey, whereas bias is more difficult. That's why, in an effective survey, we do everything possible to eliminate bias, so that the accuracy of the survey results depends on variance only. The MSM is no exception to this: by using a well-tested questionnaire, a proven methodology, specialized interviewers and strict quality control, and by following up with businesses that do not initially respond to the survey, we are able to minimize bias in the MSM.

Once we have minimized bias, we can adequately represent the accuracy of the survey results by variance only. We can express variance in various ways. For example, the August 2014 result of $52,100 million in sales of goods manufactured had a standard error of $260 million. The standard error represented 0.5% of the goods sold—this percentage is called the coefficient of variation, and is commonly used by Statistics Canada to express variance. Another method commonly used by the media to express variance is margin of error, which is also based on the standard error. With this method, the result of the August 2014 MSM could be expressed in the following familiar format: "Based on the Monthly Survey of Manufacturing, Statistics Canada estimates that $52,100 million of goods manufactured were sold in August 2014, with a margin of error of $520 million, 19 times out of 20." In this statement, the margin of error is twice the standard error.

In conclusion, bias and variance are key measures of the accuracy of survey results. When we conduct a survey using sound quality assurance principles, we avoid bias. When we design a survey on a sound scientific basis, we can calculate and control variance. Regardless of how we report variance—as a measure of the precision of survey results—the interpretation is always the same: the smaller the variance and the associated standard error, coefficient of variation and margin of error, the more reliable the corresponding survey results are considered to be.