Google Data Analytics Professional Certification Practice Test

What do you call the systematic error that occurs when the sample used does not accurately reflect the population?

1. Observer error

2. Sampling bias

3. Data bias

4. Measurement error

The correct answer is: Sampling bias

Sampling bias refers to the systematic error that occurs when the sample drawn from a population does not accurately represent that population. This can happen for a variety of reasons, such as a non-random selection process or over-representation or under-representation of certain groups within the population. For instance, if a survey only includes participants from a specific demographic that does not encompass the entire target population, the results will be skewed and may lead to incorrect conclusions. Understanding sampling bias is critical in data analytics, as it affects the validity of the findings and can compromise decision-making processes based on the analysis. Addressing sampling bias involves employing random sampling techniques or ensuring that the sample mirrors the population characteristics, thereby enhancing the reliability of the inferences drawn from the data collected. The other options, such as observer error, data bias, and measurement error, refer to different types of inaccuracies related to data collection and handling, but they do not specifically address the issue of the sample not being representative of the population.