A linear correlation does depict that in any two variables, there isa relationship between the two variables. The fact that they arerelated does not mean that one causes the other. In this case, thetwo variables are smoking and the pulse rate. The conclusion derivedis that as one increases the number of cigarettes that the smoke, thechances are that the pulse rate would equally increase. It may betrue that cigarette smoking has an impact on the rate of the pulse.However, it is not definite that rate of pulse is not only affectedby the tobacco. Other underlying factors could result in an increasein the rate of pulse apart from cigarette smoking. The error, inconclusion, is that the cigarettes smoked will cause the rate of thepulse to increase as well. The two may be related, but it does notmean that one causes the other to occur. For example, it does notmean that the cigarette smoked is the only causative factor for theincrease in the rate of pulse. Correlation may not imply causation(Grinthal & Berkeley Heights, 2015). There is a need to prove thecausal relationship through further research.
Example ofwhen statistics was used inappropraiately
An example of when statistics has been used inappropriately is whenconfusing the terms causality and correlation. Such a case is evidentwhen addressing the concept of smoking and the pulse rates. Forexample, if there is a high incidence of smoking, it is not correctto conclude that many of those who do so will have a high pulse rate.Correlation may exist between cigarette smoking and pulse rates, butit does not mean that there is a causal relationship. Having a highrate of pulse could as well be suffering from a different condition.Much research may need to be conducted to prove the causalrelationship.
Grinthal, T., &Berkeley Heights, N. J. (2015). Correlation vs. Causation. AMERICAN