I’ve been thinking about how we move from spotting a change in data to actually explaining it in a statistically sound way.

In practice, it’s easy to identify patterns, but much harder to know if they’re meaningful or just noise. I came across something called Scoop Analytics while reading about different exploration approaches, and it made me reflect on how tools surface patterns versus how we validate them.

For those with a stats background, what checks or methods do you rely on to make sure your explanations are actually robust?