What type of bias occurs when certain members of a population are less likely to be included in a sample?

The type of bias that occurs when certain members of a population are less likely to be included in a sample is known as sampling bias. This situation arises when the method used to select a sample results in some members of the population being systematically excluded or underrepresented. For example, if a researcher is conducting a survey using an online platform, individuals without internet access would be less likely to participate, leading to a sampling bias in the results.

Sampling bias affects the validity of conclusions drawn from the sample because it does not accurately reflect the characteristics of the entire population. When certain groups are overrepresented or underrepresented, the findings may be skewed, preventing valid generalizations about the population.

The other types of biases have distinct definitions and implications. Selection bias often refers to the process of deciding which individuals are included in a sample, but it typically falls under the broader category of sampling bias. Response bias pertains to inaccuracies in responses given by participants, potentially due to the way questions are phrased or the respondents' desire to present themselves in a favorable light. Confirmation bias is a cognitive bias where individuals favor information that confirms their existing beliefs rather than seeking out contrary information. Understanding these distinctions is essential for effectively identifying and mitigating different forms of bias in statistical research.