What are the types of sampling bias?

What are the different types of sampling bias?

Sampling bias occurs when certain members of a population are systematically more likely to be selected for a sample. The main types include:

1. Selection Bias: When the sample is not representative of the population.
2. Survivorship Bias: Focusing on the people or things that passed a selection process and overlooking those that did not.
3. Non-response Bias: When individuals selected for the sample do not respond, leading to a skewed representation.
4. Exclusion Bias: When certain groups are excluded from the sample.
5. Self-selection Bias: When individuals select themselves into a group, often leading to a non-representative sample.

Can you explain selection bias in more detail?

Certainly! Selection bias occurs when the individuals included in a sample are not representative of the larger population. This can happen due to various reasons, such as:

• Method of Selection: If a survey is conducted online, those without internet access are excluded.
• Voluntary Response: If participants self-select to join a study, those with strong opinions may dominate the results. This bias can lead to inaccurate conclusions and affect the validity of research findings.

What are some examples of studies affected by sampling bias?

One classic example is the 1936 Literary Digest poll, which incorrectly predicted the outcome of the presidential election. They surveyed their subscribers, who were predominantly wealthy, leading to a biased sample that did not reflect the general population. Another example is health studies that only include participants from specific demographics, which can skew results and limit generalizability.

How can researchers avoid sampling bias?

Researchers can avoid sampling bias by:

1. Random Sampling: Ensuring every individual has an equal chance of being selected.
2. Stratified Sampling: Dividing the population into subgroups and sampling from each to ensure representation.
3. Oversampling: Intentionally including more participants from underrepresented groups.
4. Using Multiple Methods: Combining different sampling techniques to balance out biases.