If two variables are consistently related to one another, you can conclude that changes in one variable are causing changes to occur in the other. a) True b) False
The correct answer and explanation is:
The correct answer is b) False.
Explanation:
Just because two variables are consistently related to one another (i.e., they show a correlation), it does not necessarily mean that one variable is causing changes in the other. Correlation does not imply causation. This is a common misunderstanding in research and statistical analysis. A correlation simply means that there is a relationship between two variables, but it does not provide any information about the direction of the relationship or whether one variable is the cause of the other.
To establish a causal relationship between two variables, several conditions must be met, and it usually requires more than just observing a correlation. One key requirement is that there must be a logical mechanism or explanation for how one variable affects the other. This could be supported by experimental designs, where variables are manipulated in a controlled setting to observe the effects. Additionally, causal relationships typically require ruling out alternative explanations, such as third variables that may influence both variables (known as confounding variables).
For example, there may be a correlation between the number of ice cream cones sold and the number of drownings in a given area. While these two variables are related, it is not the ice cream that causes drownings. Instead, both variables may be influenced by a third factor—temperature. During hot weather, more people buy ice cream, and more people go swimming, increasing the risk of drowning. This is an example of a spurious correlation.
In short, to determine causality, further investigation, including experimental methods or longitudinal studies, is needed to rule out other potential influences and confirm a cause-and-effect relationship.