Correlation is an important factor in determining the relationship of activities in real life as it measures the strength and direction of relationships or associations that exist between two variables measured on an interval scale. For instance, correlation can be used to determine whether there is a relationship between time taken to study or revise and the results attained in an exam. Longer lengths of study and revision have proven to be associated with high scores in exam because it increases the rate of underrating and grasping aspects tested in an exam. This is therefore the reason why students are encouraged to read more to enhance their understanding of subjects hence getting good scores in an exam. Correlation is used to draw a line of best fit through data of the variables in question (Warne, 2018). The correlation coefficient, r, shows how far or near all the data points appear to be from the line of best fit. Also, McClave (2018) adds that the correlation coefficient when positive indicates there the two variables have a positive correlation. On the other hand, a negative correlation coefficient shows that the two variables have a negative correlation.
Conducting a t-test is important because it helps in determining whether correlation is an outcome of significant relationship or randomness between the two variables. Therefore, researchers should use a hypothesis test to learn the relationship between the variables when the response variable is known (McClave, 2018). Consequently, in determining the relationship between length of study and exam scores, it obvious that the predictor variable is length of study while the exam scores is the response variable (Warne, 2018). Hence, this is the reason why using t-test for correlation is essential in real life applications.
McClave, J. T., & Sincich, T. (2018). A first course in statistics.
Warne, R. T. (2018). Statistics for the social sciences: A general linear model approach. Cambridge: Cambridge Univ. Press.
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