Your team collected survey data showing that departments with higher coffee consumption also report higher job satisfaction scores. A colleague suggests the company should invest in better coffee machines to boost satisfaction. What common data mistake is your colleague making?

The scenario describes a situation where two variables, coffee consumption and job satisfaction, are observed to be correlated. The colleague then assumes that increasing one (coffee consumption) will directly cause an increase in the other (job satisfaction). This is a common logical fallacy in data analysis.

• Correlation means that two variables tend to change together. For example, as coffee consumption goes up, job satisfaction also tends to go up.
• Causation means that one variable directly influences or causes a change in another.

The colleague is observing a correlation and incorrectly inferring a causal relationship. There might be other factors (confounding variables) that influence both coffee consumption and job satisfaction, or the relationship might be purely coincidental.

The correct option is:

• Confusing correlation with causation

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