Final answer:
The nature of association considering lurking variables can show confounded relations that are not directly causal. Lurking variables can lead to spurious or illusory correlations, highlighting the importance of considering these variables in study designs to prevent drawing invalid conclusions.
Step-by-step explanation:
The nature of an observed association when taking into account unobserved or lurking variables can be complex. Lurking variables are external variables that are not included in the study design but can influence the variables being studied. These lurking variables can lead to a confounded association where the observed relationship is not a direct causal effect, but rather the result of some other, possibly unnoticed, variable. For instance, if there appears to be a correlation between the number of fast-food restaurants in a neighborhood and obesity rates, a lurking variable like poverty could be responsible for both the prevalence of fast-food restaurants and higher obesity rates, distorting the perceived relationship between the two directly studied variables.
An experimental design that randomly assigns subjects to treatment groups helps control for lurking variables. Without proper control, we might incorrectly assume causality from correlation, leading to spurious relationships or illusory correlations. For example, despite common beliefs, there is no scientific evidence to back the claim that human behavior is influenced by the phases of the moon, which is an instance of an illusory correlation.
Therefore, it is crucial to consider potential lurking variables in observational studies to avoid drawing invalid conclusions about causation from mere correlation.