Final answer:
Non-response bias, self-selection bias, and healthy worker effect are all forms of selection bias that can result in unrepresentative study samples and skewed data. These biases can significantly impact the validity of research findings.
Step-by-step explanation:
Understanding Different Types of Bias
Non-response bias occurs when the individuals selected for a survey or study do not all respond, and the responses that are collected no longer reflect the opinions or behaviors of the entire population. This can significantly affect the results, often because people with strong opinions may be more likely to respond, skewing the data.
Self-selection bias, also known as volunteer bias, happens when participants in a study are not randomly selected but instead volunteer themselves. This can lead to a sample that is not representative of the entire population as the individuals who volunteer might share certain characteristics that are not prevalent in the wider group. For example, internet surveys can be subject to self-selection bias as people choose whether or not to respond.
Healthy worker effect is a phenomenon observed in occupational epidemiology, where workers generally exhibit lower overall mortality rates than the general population because the very ill and chronically disabled are not among the employed.
All these biases fall under the category of selection bias, and they occur when the method of selecting participants or collecting data causes the sample to be unrepresentative of the population being studied, leading to inaccurate results or conclusions.