**Core Concept**
The association between two variables can be explained by a third variable, a concept that highlights the importance of considering confounding factors in statistical analysis and clinical research. This phenomenon is a fundamental aspect of epidemiology and biostatistics, where it is essential to identify potential confounders that may influence the observed relationship between variables.

**Why the Correct Answer is Right**
When two variables appear to be associated, it is crucial to investigate whether a third variable can explain this relationship. This third variable, also known as a confounder, can be a risk factor, a mediator, or an effect modifier that affects the outcome variable. For instance, in a study examining the relationship between smoking and lung cancer, a third variable such as age could be a confounder if the risk of lung cancer increases with age and smoking is more prevalent among older individuals.

**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because it does not acknowledge the importance of considering confounding variables in statistical analysis.
**Option B:** This option is incorrect because it suggests that the association between two variables is always due to a third variable, which is not the case.
**Option C:** This option is incorrect because it implies that a third variable can only explain a positive association between two variables, whereas confounders can also explain negative or neutral associations.

**Clinical Pearl / High-Yield Fact**
One key aspect to remember is that confounders can be either positive or negative, and it's essential to identify and adjust for them in statistical analysis to avoid biased conclusions. This can be achieved through techniques such as stratification, matching, or multivariable regression.

**Correct Answer: D.**