Final answer:
Confounding variables are additional factors that affect the relationship between two variables. Two examples include third variables that are related to both variables of interest and selection bias in study participants.
Step-by-step explanation:
In observational studies, confounding variables are additional factors that affect both the variables of interest and can explain the correlation between them. These variables may lead to a misunderstanding of the relationship between the two variables. Here are two examples of confounding variables affecting the relationship:
- Third Variable: A third variable that is related to both variables of interest can confound the relationship. For example, in a study examining the relationship between coffee consumption and heart disease, age could be a confounding variable. Both coffee consumption and heart disease rates may be higher in older adults, making age the confounding variable.
- Selection Bias: When participants are not randomly assigned to groups in a study, selection bias can occur. This can result in differences between groups that are not due to the variables being studied. For instance, if participants self-select into two groups based on their dietary habits, there may be confounding variables affecting the relationship between diet and health outcomes.