What are the different 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. Here are some common types:

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 sample.
4. 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?

Sure! Selection bias occurs when the method of selecting participants for a study results in a sample that is not representative of the population. This can happen due to various reasons, such as only including volunteers or only surveying a specific demographic. For example, if a survey about health is conducted only in a gym, it may not accurately reflect the health behaviors of the general population.

What are some ways to avoid sampling bias?

To avoid sampling bias, researchers can:

1. Use random sampling methods to ensure every individual has an equal chance of being selected.
2. Stratify the sample to include different subgroups of the population.
3. Ensure that the sample size is large enough to represent the population accurately.
4. Use multiple methods of data collection to capture a broader perspective.

Can you give an example of survivorship bias?

Certainly! A classic example of survivorship bias is the analysis of World War II bomber planes. Researchers initially focused on the planes that returned from missions, analyzing where they were hit to reinforce their armor. However, they overlooked the planes that didn't return, which may have been hit in different areas. This led to a flawed conclusion about where to add armor, as the analysis only considered the 'survivors'.