In Exercises 21–24, refer to the data in the table below. The entries are white blood cell counts (1000 cells,ML) and red blood cell counts (million cells,ML) from male subjects examined as part of a large health study conducted by the National Center for Health Statistics. The data are matched, so that the first subject has a white blood cell count of 8.7 and a red blood cell count of 4.91, and so on.

Conclusion: If we analyze the sample data and conclude that there is a correlation between white blood cell counts and red blood cell counts, does it follow that higher white blood cell counts are the cause of higher red blood cell counts?

Result of statistical analysis: Correlation between two types of blood cell counts

Correlation is a measure that is used to estimate the level of linearity between two variables. It helps to signify the direction as well as the magnitude of a linear association.

In the case of two correlated variables, the cause of correlation is not assured. It cannot be indicated that correlation occurs because one variable causes the other to change.

In this case, despite the evident correlation between blood counts, it is incorrect to conclude that one variable causes the change in the other variable. Hence, it cannot be concluded that a higher white blood cell count in males can cause the count of red blood cells to increase.