A community garden records more visitors in the same weeks that it sells more cold drinks. It would be easy to say the drinks attracted the visitors. Warm weather, a special event or longer opening hours might also explain the pattern.
This invented example shows why an association is the beginning of an investigation rather than a complete causal explanation.
Describe the relationship without overstating it
The Australian Bureau of Statistics distinguishes correlation from causation and warns that related changes do not by themselves establish cause and effect.
Start with the narrower claim: weeks with more visitors also had higher drink sales. That statement leaves room to investigate what connects the two.
List plausible alternative explanations
Ask what else changed, whether one measure partly contains the other, and whether the observations were selected in a way that creates a misleading pattern. A larger crowd has more potential buyers even if drinks do not influence attendance.
Writing down alternatives is not proof that they are correct. It helps identify evidence that could distinguish between them.
Look at timing and study design
Did the supposed cause occur before the outcome? Was there a comparison group or another design intended to separate competing explanations? What limitations do the researchers themselves describe?
Do not assume that one statistical coefficient answers every question about the relationship. The collection method and the wider evidence matter.
Match your conclusion to what was tested
An observational pattern may justify further research or a carefully designed experiment. It does not automatically justify changing policy or claiming that an intervention worked.
When reading a dataset, use our provenance questions to understand how the observations were produced. A clear statement of what remains unknown is often the most valuable result of a first analysis.