What statement best defines sampling bias?

• A. Random variation in sample outcomes due to chance.
• B. Systematic error due to design that makes the sample non-representative of the population.
• C. A sample chosen as the easiest to obtain.
• D. A sample where every member has the same probability of selection.

Answer: (B)

Sampling bias arises when the way you pick the sample introduces a systematic error that makes the sample non-representative of the population. It’s not about random luck or natural variation in outcomes; it’s about the design or conduct of the sampling process that consistently favors certain individuals or groups over others. This is what can lead to conclusions that don’t reflect the true characteristics of the whole population. For example, surveying only people who are easily reachable or who frequent a particular location can overrepresent some groups and underrepresent others, introducing bias. In contrast, a plan where everyone has the same chance of selection aims to avoid bias, while random variation due to chance is something expected even in well-designed samples. So the core idea is a systematic error from the sampling design that makes the sample fail to represent the population.