Sampling bias occurs when the sample is not representative of the population being studied. There are several types of sampling biases:

1. Selection bias: This occurs when the sample is not randomly selected from the population. For example, if a survey is conducted only among people who are willing to participate, it may not be representative of the entire population.

2. Non-response bias: This occurs when the sample is not representative because some individuals refuse to participate. For example, if a survey is conducted among students, but only those who are present in class are included, it may not be representative of the entire student population.

3. Volunteer bias: This occurs when individuals who volunteer to participate in a study are different from those who do not volunteer. For example, if a study on health behaviors is conducted only among those who are interested in health, it may not be representative of the entire population.

4. Sampling frame bias: This occurs when the sampling frame, or list of individuals or units from which the sample is selected, is not representative of the population. For example, if a survey is conducted among registered voters, but some eligible voters are not registered, it may not be representative of the entire voting population.

5. Measurement bias: This occurs when the measurement instrument used to collect data is flawed or biased. For example, if a survey question is worded in a way that is confusing or misleading, it may not accurately measure the intended construct.