When does bias in sampling typically occur?

Bias in sampling typically occurs when selected elements do not represent the larger population. This situation arises when the sampling process systematically favors certain groups over others, leading to an unrepresentative sample. For instance, if a researcher only surveys individuals from a specific geographic area or demographic group that does not reflect the broader population's diversity, the insights gained may be skewed and not applicable to the entire population.

A sample that is not representative can lead to erroneous conclusions and may affect the validity of research findings. This explains why ensuring representativeness in sample selection is crucial in research methodologies, especially in social work practice, where understanding the needs of diverse populations is vital for effective interventions. Bias can manifest in various forms, including selection bias, where certain characteristics of the population are over-represented or under-represented in the sample.

In contrast, when participants are randomly selected, the likelihood of bias decreases as it enhances the representativeness of the sample. If the sample accurately reflects the population, the findings are more likely to be generalizable. Additionally, using a large sample does not inherently minimize bias, as even large samples can be biased if not properly randomized. Therefore, it is the representativeness of the sample that plays a crucial role in minimizing