Scientists plotted data for two animal populations on a scatterplot: Population A, which they graphed along the x-axis, and Population B, which they graphed along the y-axis. The data showed a strong negative correlation. Which of the following statements is justified?
A) The rise in Population A caused the decline in Population B.
B) The decline in Population B caused the rise in Population A.
C) Because the correlation is negative, there cannot be causation between the two populations.
D) The rise in Population A is correlated to the decline in Population B, but causation is unknown.
Answer (1)
In the context of a scatterplot showing a strong negative correlation between two populations, we need to carefully analyze the relationship between these variables.
A negative correlation means that as one variable increases, the other variable tends to decrease. In this scenario, as Population A (graphed along the x-axis) increases, Population B (graphed along the y-axis) decreases.
Let's evaluate the given statements:
A) 'The rise in Population A caused the decline in Population B.' - This statement implies a cause-and-effect relationship. However, correlation alone does not imply causation, so this cannot be conclusively justified.
B) 'The decline in Population B caused the rise in Population A.' - Similar to statement A, this implies causation from correlation, which is not justifiable with correlation alone.
C) 'Because the correlation is negative, there cannot be causation between the two populations.' - This is incorrect. While correlation does not imply causation, it does not rule out causation either. Other factors or controlled experiments would need to confirm causation.
D) 'The rise in Population A is correlated to the decline in Population B, but causation is unknown.' - This is the most accurate statement. It acknowledges the correlation but also recognizes that without further investigation, causation cannot be established.
Therefore, the justified statement is D: The rise in Population A is correlated to the decline in Population B, but causation is unknown.
This teaches us an important lesson about data analysis: correlation helps identify relationships between variables, but further research is necessary to determine if there is a causal relationship.
