Which of the following would best avoid a causal fallacy when interpreting data showing correlation?

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When interpreting data that show a correlation, the best way to avoid a causal fallacy is to show a plausible mechanism that links the variables. Demonstrating a mechanism provides a concrete process by which one variable could influence the other, making a causal claim more credible than simply noticing that two things move together. For example, if higher stress is correlated with poorer sleep, a mechanism could be that stress increases cortisol, which disrupts sleep patterns. This kind of explanation gives a believable pathway from cause to effect rather than relying on a mere association.

Other approaches fall short because they either imply causation from correlation without additional support, rely on too little data to be confident, or ignore confounding variables that could be driving both things at once.