Sampling bias refers to a bias that occurs when the sample selected to represent a population is not representative of the population. This can occur due to various reasons, such as:

1. Non-probability sampling methods: When the sampling method used is not random, the sample may not be representative of the population. For example, if a researcher only samples individuals who are easily accessible, the sample may not represent the entire population.

2. Selection bias: When the sample is selected based on certain characteristics, the sample may not be representative of the population. For example, if a researcher only samples individuals who are willing to participate in a study, the sample may not represent the entire population.

3. Sampling frame bias: When the sampling frame used to select the sample is incomplete or inaccurate, the sample may not be representative of the population. For example, if a researcher uses a phone directory to sample individuals, the sample may not represent the entire population because not everyone has a phone.

4. Response bias: When the responses given by the participants are not accurate or truthful, the sample may not be representative of the population. For example, if a researcher asks participants about their drug use, some participants may not disclose their drug use due to fear of legal repercussions.

5. Volunteer bias: When the sample only includes individuals who volunteer to participate in a study, the sample may not be representative of the population. For example, if a researcher only samples individuals who volunteer for a study on depression, the sample may not represent the entire population because not everyone with depression may be willing to participate in a study.