Google Data Analytics Professional Certification Practice Test

Which of the following best describes "sampling bias"?

1. A method that ensures accurate sample representation

2. A systematic error in selecting samples

3. A technique for increasing sample size

4. A strategy for randomizing data collection

The correct answer is: A systematic error in selecting samples

Sampling bias refers to a systematic error that occurs when certain members of a population are less likely to be included in a sample than others, leading to a skewed representation of that population. This can occur due to various reasons, such as a flawed sampling method or researcher preferences that unintentionally favor certain groups. When sampling is biased, the results and inferences drawn from the analysis may not accurately reflect the true characteristics or behaviors of the entire population. For example, if a researcher only surveys individuals who are available during the day, they might miss out on viewpoints from those who work during the day, which can significantly affect the findings. This systematic error undermines the validity and reliability of the data analysis. In contrast, options that suggest methods for accurate representation, increasing sample size, or randomizing data collection do not align with the definition of sampling bias because they focus on improving sampling techniques rather than describing errors that arise during the sampling process. Understanding and addressing sampling bias is crucial in data analytics to ensure that the conclusions drawn from a sample can be generalized to the broader population.