Differentiating noise from trends in data

Noise is a sudden spike or dip in the data, and can be easy to misinterpret as a signal towards a larger trend. In statistics, trend analysis is a rigorous way to evaluate the relationship between two variables (e.g. to see if something really does go up or down over time). Although you don’t need to be a statistician to spot a correlation, it is a lot more tempting to label something as a trend when something has ticked up a couple of times without the right tools.

I usually gut-check by looking for the following:
a) time frames (does monthly, quarterly, or yearly make sense for this context?),
b) patterns (3+ points in the same direction, vs 1-2 points), and
c) frame of reference: what does “normal” look like?

What’s your approach to differentiating trends from noise?