Google Data Analytics Professional Certification Practice Test

Sampling bias occurs when:

1. A sample is small and irrelevant

2. A sample isn't representative of the entire population

3. All data is collected through a single method

4. The sample size is excessively large

The correct answer is: A sample isn't representative of the entire population

Sampling bias occurs when a sample isn't representative of the entire population. This means that the characteristics of the sample do not accurately reflect those of the population from which it was drawn, leading to results that may not be generalizable. This can happen for various reasons, such as the method of selecting participants or inherent traits that make a subset of the population overrepresented or underrepresented in the sample. For instance, if a survey is only administered to a particular demographic group that does not represent the overall population, the findings may skew toward the preferences or behaviors of that specific group. This limitation can result in misleading conclusions and impact the validity of any analyses drawn from that data. Other factors in the options, such as a sample being small and irrelevant, collecting data through a single method, or having a sample size that is excessively large, may present their own challenges in research. However, they do not inherently create sampling bias in the same way that a lack of representativeness does. A small sample might still be representative, just less reliable, while a large sample may still produce biased results if it's not representative. Similarly, using a single method could lead to methodological bias rather than sampling bias. Thus, the essence of sampling bias lies in the representativeness