Sales of ography and the presence of churches in the area appear to be correlated. It may be the case that neither causes the other. Perhaps a separate factor, such as population density, accounts best for both aggression and ice cream sales. This illustrates a problem in assessing causality that is specific to correlational research, and is called __________________________.
I have no idea what kind of answer the question is asking for. May someone please help?
<<The counter-assumption, that "correlation proves causation," is considered a questionable cause logical fallacy in that two events occurring together are taken to have a cause-and-effect relationship. This fallacy is also known as hoc ergo propter hoc, Latin for "with this, therefore because of this," and "false cause." A similar fallacy, that an event that follows another was necessarily a consequence of the first event, is sometimes described as post hoc ergo propter hoc (Latin for "after this, therefore because of this.").>> From Wikipedia.
The answer is in your text. Review the section of the chapter that discusses cause and effect. This may elaborate on the concept:
http://explorable.com/cause-and-effect
But, remember, the terminology in this article may vary from what your text materials use.
The problem being described in the question is known as the "third variable problem" or "confounding variable." In correlational research, when two variables appear to be related, it is essential to consider the possibility that there may be a third factor or variable that is actually responsible for the observed relationship between the two variables.
In this particular example, there seems to be a correlation observed between the sales of ography and the presence of churches. However, it is mentioned that population density could potentially be the underlying factor responsible for both aggression and ice cream sales, thus confounding the relationship between ography sales and the presence of churches.
To understand and assess causality accurately, it is important to consider additional factors (potential confounding variables) that could influence or explain the relationship between the two variables of interest. This highlights the need for more rigorous research designs, such as experimental studies, that can better control for confounding variables and provide stronger evidence for causal relationships.