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https://math.answers.com/math-and-arithmetic/How_can_variables_be_correlational_but_not_causal

How can variables be correlational but not causal? - Answers

Variables can be correlational but not causal when they show a statistical relationship without one directly influencing the other. This can occur due to confounding factors that affect both variables or due to coincidence in data patterns. For example, ice cream sales and drowning incidents may correlate during summer months, but neither causes the other; both are influenced by the warmer weather. Thus, correlation does not imply causation without further evidence.



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How can variables be correlational but not causal? - Answers

https://math.answers.com/math-and-arithmetic/How_can_variables_be_correlational_but_not_causal

Variables can be correlational but not causal when they show a statistical relationship without one directly influencing the other. This can occur due to confounding factors that affect both variables or due to coincidence in data patterns. For example, ice cream sales and drowning incidents may correlate during summer months, but neither causes the other; both are influenced by the warmer weather. Thus, correlation does not imply causation without further evidence.



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https://math.answers.com/math-and-arithmetic/How_can_variables_be_correlational_but_not_causal

How can variables be correlational but not causal? - Answers

Variables can be correlational but not causal when they show a statistical relationship without one directly influencing the other. This can occur due to confounding factors that affect both variables or due to coincidence in data patterns. For example, ice cream sales and drowning incidents may correlate during summer months, but neither causes the other; both are influenced by the warmer weather. Thus, correlation does not imply causation without further evidence.

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      Variables can be correlational but not causal when they show a statistical relationship without one directly influencing the other. This can occur due to confounding factors that affect both variables or due to coincidence in data patterns. For example, ice cream sales and drowning incidents may correlate during summer months, but neither causes the other; both are influenced by the warmer weather. Thus, correlation does not imply causation without further evidence.

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