This story is a playful example of confounding:
> In causal inference, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system. / Confounds are threats to internal validity.
https://en.wikipedia.org/wiki/Confounding
Here is a sketch of a statistical model that shows a confounder (a variable affecting both the dependent and independent variables)
S = f(H, I, T)
`S`: car starting or not (dependent variable)
`H`: how hot is the car engine (independent variable)
`I`: ice cream type chosen (independent variable)
`T`: time taken to buy the ice cream (a confounder)
Explanation: `T` influences `S` because a shorter time leads to `H` (a hotter engine, which is prone to vapor lock). And `T` also influences `I` (type of ice cream chosen) because the placement of vanilla ice cream allows for quicker purchase. Voila, now we have a spurious relationship between `I` and `S`.
> In causal inference, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system. / Confounds are threats to internal validity.
https://en.wikipedia.org/wiki/Confounding
Here is a sketch of a statistical model that shows a confounder (a variable affecting both the dependent and independent variables)
Explanation: `T` influences `S` because a shorter time leads to `H` (a hotter engine, which is prone to vapor lock). And `T` also influences `I` (type of ice cream chosen) because the placement of vanilla ice cream allows for quicker purchase. Voila, now we have a spurious relationship between `I` and `S`.