Question

A researcher is interested in explaining variation in people's incomes. He hypothesizes that income is caused by number of friends in a person's social network. He conducts a correlation analysis (Pearson's r) between personal income (Y) and number of friends a person has (X). He finds a strong, statistically significant relationship between the two variables. The computation of Pearson's r and hypothesis testing statistics are correct and this decision the decision to reject the null is correct. Bases on these results, he then draws the additional conclusion: "Therefore, it is clear that the number of friends a person has causes a person's income level."

Please evaluate the researcher's additional conclusion/interpretation: a. Is it appropriate or not appropriate for the research to make this additional claim based on the test used? Why or why not?

Answer 1

If the correlation is significant then the only linear relationship is meaningful but we can not draw any conclusion on causation of two variables merely based on correlation. So additional interpretation of researcher in not correct.

Explanation:

Correlation and causation are terms which are mostly misunderstood and often used interchangeably. Understanding both the statistical terms is very important not only to make conclusions but more importantly, making correct conclusion at the end. Correlation is a statistical technique which tells us how strongly the pair of variables are linearly related and change together. It does not tell us why and how behind the relationship but it just says the relationship exists.

Causation takes a step further than correlation. It says any change in the value of one variable will cause a change in the value of another variable, which means one variable makes other to happen. It is also referred as cause and effect.