The third criterion for causality is also the most troublesome, as it requires that alternative explanations for the observed relationship between two variables be ruled out. This is termed non-spuriousness, which simply means “not false.” A spurious or false relationship exists when what appears to be an association between the two variables is actually caused by a third extraneous variable. Classic examples of spuriousness include the relationship between children’s shoe sizes and their academic knowledge: as shoe size increases so does knowledge, but of course both are also strongly related to age. Another well-known example is the relationship between the number of fire fighters that respond to a fire and the amount of damage that results – clearly, the size of the fire determines both, so it is inaccurate to say that more fire fighters cause greater damage. Though these examples seem straightforward, researchers in the fields of psychology, education, and the social sciences often face much greater challenges in ruling out spurious relationships simply because there are so many other factors that might influence the relationship between two variables. Appropriate study design (using experimental procedures whenever possible), careful data collection and use of statistical controls, and triangulation of many data sources are all essential when seeking to establish non-spurious relationships between variables.