Final answer:
Option (B), Correlation indicates a statistical association between two variables, but it does not prove causation, which is when one variable is responsible for causing the change in the other.
Step-by-step explanation:
Understanding Correlation and Causation
When it comes to non-experimental or correlational research methods, it is crucial to discern the difference between correlation and causation. Correlation signifies that there is a statistical association between two variables, which means when one variable changes, the other variable tends to change as well. However, correlation does not imply causation; it does not prove that one variable causes the change in the other. Causation, on the other hand, indicates that one variable is responsible for causing the change in the other variable.
To illustrate, if ice cream sales increase during the summer months, and simultaneously, the crime rate also goes up, there is a correlation between ice cream sales and crime rates. Nonetheless, this does not establish causation. A confounding variable, like higher temperatures, could explain both increased ice cream sales and crime rates, without one directly causing the other. This example underscores how critical it is to remember the correlation-causation fallacy and why experiments must carefully control for confounding variables.
The role of a control group is also vital in experimental research to manage for potential confounding variables, thereby contributing to the reliability of experimental results by holding conditions constant except for the variable being tested. Correlational studies, since they do not involve such control or manipulation, typically cannot determine cause-and-effect relationships, leaving the researcher to observe and report associations between variables.