Spurious Versus Surpressor Relaitonships

Introduction and Statement of the Problem

"Spurious significance" is a term in statistics used to describe a situation when a statistic returns a value which is "statistically significant", when it is impossible that there is any significance. It’s a topic, which sounds easy, but quickly gets difficult. There are correlations that can be found that to be causal in nature but in reality have no basis in facts and are due to a third variable not easily seen. The true reason for a relationship whether positive or negative for variables can be found if we examine other surrounding variables that can be explored as possible direct causes of said relationships. The underlying cause of the relationship must be fleshed out so as to understand the true nature of the relationship and the subject. There are both spurious relationships and one must use statistical graphs in order to see visually how the two or three variables affect one another and we will also use them to find reasonable inferences as to cause and effect in these relationships. The purpose of this paper is to illustrate the widespread occurrence of spurious correlations and the mistake often made that they are confused with suppressor relationships.

Literature Review and Examples of Each

Suppressor relationships are false indications of no causality. We will explain suppressor relationships and make the clear distinction between spurious and suppressor relationships.

If the control variable is the cause for both the original independent and dependent variable, you have a spurious relationship: the original relationship was an illusion. It "looked real" but was a numeric artifact. It occurred only because the control variable caused both the original "independent" (which was really a dependent variable too in a sense) and the "dependent" variables.

Use of the examples to illustrate spurious relationships: City Manager Sapp, of Sappville, has collected data on the past 100 fires to occur in the...