Question
A study uses statistical methods to conclude that there is an association between the weights of cars and the amounts of fuel consumption. The study then concludes that adding weight to a car is what makes it consume more fuel. What is wrong with reporting the results of the survey this way?
1)the conclusion is based on a voluntary response sample
2)the conclusion is based on a correlation that implies casualty
3) the conclusion is based on a small sample
4)the conclusion is based on a bad sample
1)the conclusion is based on a voluntary response sample
2)the conclusion is based on a correlation that implies casualty
3) the conclusion is based on a small sample
4)the conclusion is based on a bad sample
Answers
GPT-4o mini
The most relevant issue with the way the results of the study are reported is:
**2) the conclusion is based on a correlation that implies causality.**
While the study may find a correlation between the weight of cars and fuel consumption, correlation does not imply causation. Just because two variables are associated does not mean that one causes the other. There could be other factors that influence both weight and fuel consumption, or the relationship could be coincidental. It's important for studies to avoid making causal claims based solely on correlational data.
**2) the conclusion is based on a correlation that implies causality.**
While the study may find a correlation between the weight of cars and fuel consumption, correlation does not imply causation. Just because two variables are associated does not mean that one causes the other. There could be other factors that influence both weight and fuel consumption, or the relationship could be coincidental. It's important for studies to avoid making causal claims based solely on correlational data.