A bias is an error that results in a misrepresentation of a whole population.
See solution.
Practice makes perfect
We want to find out whether booth holders at a convention were pleased with their booth locations. To do so, we divide the convention center into six sections and survey every booth in the fifth section. Here, the population consists of all booth holders at the convention and the sample of the booth holder in the fifth section. Let's recall how samples can be classified.
Members that are readily available or easy to reach are selected.
We divide the convention center into six sections and survey only booth holders from the fifth section. Therefore, this is cluster sample. The sample is biased because the booth holders in the one selected section can have completely different opinion about their location than holders from other sections.
