Final answer:
The scenario with the manager at an Apple store allowing some customers to try out new Air Pods potentially introduces experimental design bias. This is distinct from sampling biases, such as random sampling bias or convenience sampling bias. It's crucial for an experiment's validity that subjects are randomly assigned to treatment groups to ensure a fair comparison and prevent bias.
Step-by-step explanation:
In the scenario presented, the manager's decision to allow only some customers to try out the new Air Pods, while not offering this opportunity to others, may introduce a type of sampling bias known as experimental design bias. This is not a sampling issue where the data is collected from a population, but rather an experimental manipulation that could affect the outcome of the sales numbers, making it distinct from random sampling bias, convenience sampling bias, or selection bias. The key in maximizing the validity of an experiment is to ensure that the allocation of subjects to different treatments (trying the Air Pods versus not) is done using random assignment. This allows for an even distribution of other variables that could impact the outcome and provides a fair comparison between the two groups.
In terms of types of survey design, the example from the question where a manager selects departments at random and then interviews all employees within those departments is cluster sampling. Convenience sampling is a non-random sampling technique that may introduce bias by selecting readily available subjects rather than using a system that ensures that every possible subject has an equal chance of being selected. True random sampling is often difficult to achieve, and convenience samples might be used as a second-best option, although they do come with risks of bias.