Final answer:
C: Sample bias is experienced when collected data does not accurately reflect the entire population because some members are less likely to be chosen. This can lead to incorrect conclusions about the population and is mitigated by using randomized and inclusive sampling methods.
Step-by-step explanation:
When your collected data doesn't accurately reflect the full environment due to some members of the population being less likely to be chosen, you are experiencing C: Sample bias. This occurs when the method of selecting a sample causes it to be unrepresentative of the population. For example, conducting a survey of all students only during noon lunchtime will inevitably exclude those who do not have lunch at that time. It is crucial for researchers to attempt to include all relevant members of the population to avoid sampling errors and selection bias.
Having a large sample size does not necessarily protect against bias if the sample is not random or if it systematically excludes certain groups. Methods like random sampling and ensuring that the survey is accessible at various times are ways to mitigate sampling bias.