Final answer:
The interpretation of a correlation coefficient and its corresponding p-value depends on the values provided, which are not explicit in the question. Generally, a correlation coefficient close to 1 or -1 with a low p-value indicates a strong and significant relationship between two variables.
Step-by-step explanation:
Without the exact correlation coefficient and p-value being provided in the question given, we cannot definitively identify which statement is the correct interpretation of the correlation coefficient and p-value. However, we can explain how to interpret these values in general and then see if enough information can be gleaned to make an educated guess.
The correlation coefficient measures the strength and direction of the linear relationship between two variables. A coefficient close to 1 or -1 signifies a strong relationship, while one close to 0 signifies a weak relationship. The sign of the correlation coefficient indicates the direction of the relationship: a positive value implies that as one variable increases, the other also tends to increase, whereas a negative value implies an inverse relationship between the variables.
The p-value helps us determine the statistical significance of our results. If a p-value is lower than the established level of significance (usually 0.05), we reject the null hypothesis, which typically states there is no effect or no relationship, and conclude that the correlation coefficient is significant. This would mean that the relationship observed is unlikely to have occurred by random chance.
To summarize, for the correlation and p-value mentioned in the question, if we know both the correlation coefficient's value and whether the p-value is above or below the level of significance, we can accurately state if the correlation is strong or weak, positive or negative, and significant or not significant.