Causation And Correlation – Article Example

Causation/Correlation and Necessary/Sufficient Conditions The identification of two or more data points’ relationships to each is relatively easy; however, the existing connection does not mean that each data point caused each other. One data point may singly or collectively with other data points lead to another data point/points. Thus, to fully understand the relationship, its strength and direction, and the effect/influence of one data point to another, one needs to understand the terms causation, correlation, necessary conditions and sufficient conditions.
Correlation refers to a connection between two variables/phenomena, that can be expressed mathematically, that have a tendency of fluctuating in a pattern not based on chance alone (Nadar 2011: 210). On the other hand, causation describes the agency or act of a variable producing an effect that is, one variable determining the pattern of another. This is to say, one event transpires because of another event; the cause event occurs before the effect event and in most cases, the reversal is impossible (Nadar 2011: 210). Correlation can be clearly illustrated in the business field by the connection between lemon transportation and road accident fatalities; the more lemons are transported, the higher the number of road accident fatalities. The connection between the number of hours worked and wages earned best explains causation; the more hours worked the higher the wage while the lesser the number of hours the lesser the wage, but lesser wage does not imply working for lesser number of hours.
A necessary condition is one whose absence/spuriousness guarantees the absence/spuriousness of the second condition (Swartz 1997: n.p.). A sufficient condition is one whose presence guarantees the presence of the second condition (Swartz 1997: n.p.). These two concepts can be explained by the relationship between raw materials and finished products. As necessary conditions, absence of raw materials leads to no finished products but as a necessity, raw materials alone cannot produce finished products, other factors are required.
The significance of understanding these concepts are as follows: causation and correlation helps us understand the effects of one variable on the other and facilitate further testing and optimization respectively. Necessary and sufficient conditions help us identify factors that interrupt production when present/absent.
Nadar, E. N. (2011). Statistics. New Delhi, PHL Learning.
Swartz, N. (1997). The Concept of Necessary Conditions and Sufficient Conditions. Web 18 May 2014. <>.