Possible Relationships between Variables

When the null hypothesis has been rejected for a specific α value, any of the following five possibilities can exist.

1.       There is a direct cause-and-effect relationship between the variables.  That is, x causes y.  For example, water causes plants to grow, poison causes death, and heat causes ice to melt.

2.       There is a reverse cause-and-effect relationship between the variables. That is y causes x.  For example, suppose a researcher believes excessive coffee on consumption causes nervousness, but the researcher fails to consider that the reverse situation may occur.  That is, may be that an extremely nervous person craves coffee to calm his or her nerves.

3.       The relationship between the variables may be caused by a third variable. For example, if a statistician correlated the number of deaths due to drowning and the number of cans of soft drink consumed daily during the summer, he or she would probably find a significant relationship.  However, the soft drink is not necessarily responsible for the deaths, since both variables may be related to heat and humidity.

4.       There may be a complexity of interrelationships among many variables.  For example, a researcher may find a significant relationship between students’ high school grades and college grades.  But there probably are many other variables involved, such as IQ, hours of study, influence of parents, motivation, age, and instructors.

5.       The relationship may be coincidental. For example, a researcher may be able to find a significant relationship between the increases in number of people who are committing crimes.  But common sense dictates that any relationship between these two values must be due to coincidence.

By: Marie Louissie Ynez U. Lavega