Biased sampling occurs when the sample that is selected from a population is not representative of that population. In other words, certain members of the population have a higher chance of being chosen than others, which leads to results that can be skewed or misleading.
For example, imagine a survey conducted to understand the eating habits of college students, but the sample only includes students from one specific department. This biased sample may not accurately reflect the eating habits of all college students, as other departments might have different preferences and behaviors.
Biased sampling can arise due to various factors, including the method of selection, location, or timing of the data collection. It’s essential to identify and minimize bias in sampling to ensure that conclusions drawn from the results are valid and applicable to the broader population being studied.