Question

• Life satisfaction and experience of daily stress: r = −.57 (p = .01)

• Number of friends one has and experience of daily stress: r = .09, not sig.
• Number of friends one has and life satisfaction: r = .36 (p = .04)
Which of the following conclusions can Dr. Guidry draw about the number of friends one has and life satisfaction based on her statistical analyses?
Select one:
a. The probability of her sample coming from a zero association population is about 4%.
b. The probability of her sample coming from a zero association population is about 96%.
c. The strong correlation means that the number of friends one has causes an increase in life satisfaction.
d. The relationship is not statistically significant.

Answer 1

Dr. Guidry can conclude that there is a statistically significant positive correlation between the number of friends and life satisfaction, with a correlation coefficient of .36 and a p-value of .04, indicating a low likelihood of the sample coming from a population with no correlation.

Step-by-step explanation:

Dr. Guidry can draw the conclusion that there is a statistically significant, moderate positive correlation between the number of friends one has and life satisfaction, given the correlation coefficient r = .36 and the p-value = .04. The p-value indicates that there is only a 4% probability that the observed correlation could occur if, in fact, there was no actual correlation in the population (i.e., a zero association population). Hence, the correct conclusion is that there is sufficient evidence to reject the null hypothesis of zero association, leading to the conclusion that the number of friends is statistically significantly correlated with life satisfaction.

However, it is essential to point out that correlation does not imply causation; thus, Dr. Guidry cannot conclude that having more friends causes an increase in life satisfaction, only that they are associated. This inference is based on correlational research principles, which do not establish cause-and-effect relationships, but rather the strength and direction of the relationship between two variables.