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In the context of this study concerning high blood pressure and binge drinking, the explanatory variables are the factors that the researchers believe could influence the outcome. Here, the explanatory variables are high blood pressure and binge drinking. These are the variables that are expected to have an impact on the risk of death from a stroke or heart attack.
These are referred to as explanatory because they help in explaining the variations observed in the response variable. By analyzing these variables, researchers aim to understand the conditions that might affect the likelihood of the studied outcomes occurring. Explanatory variables are pivotal in guiding the direction of a study and help in forming the basis of the hypotheses being tested.
It's important to note that in observational studies like this one, the explanatory variables are merely observed rather than controlled or manipulated by the researchers. So, while they are central to the study's inquiry, they do not possess an inherent proof-giving capacity about causation.

The response variable in any study displays the outcome or the effect that the explanatory variables are presumed to influence. In this study, the response variable is the risk of dying from stroked or heart attacks. Essentially, it is the variable of interest that researchers are keen to observe and measure.
To simplify, this is the 'result' part of the experiment or study. Researchers seek to understand how the explanatory variables (high blood pressure and binge drinking) could potentially affect this particular outcome. In the realm of data analysis, the response variable's changes or occurrences directly depend on the explanatory variables, helping researchers to identify any significant patterns or relationships.
This distinction is vital, especially in observational studies, because understanding the intricacies of how the response variable behaves, assists in drawing insightful conclusions—though, with limitations, given the lack of randomization and manipulation.

A frequent point of confusion in studies is distinguishing between correlation and causation. Correlation means that two variables appear to have a relationship, whereas causation indicates that one variable actually produces an effect on the other. In the context of this study, the observation that high blood pressure and binge drinking appear to increase the risk of death from a stroke or heart attack is a correlation.
The critical aspect here is understanding that the study, by being observational, highlights a relationship but does not prove that one action directly caused the outcome. This is because there could be other lurking variables, known as confounding variables, that could also affect the outcome. For example, lifestyle factors or genetic predispositions might play a role but aren't accounted for directly in the study.
Experimental studies, unlike observational ones, allow for more control over variables, which helps in establishing a causative relationship. But in this case, since no such control was executed, it remains a correlation, emphasizing the association without asserting definitive causation.