The data was sound. They formed a hypothesis. They got a better explanation. Now they have a better hypothesis for the data anamoly. I don't think it's fair to fault them or to discredit the effort.
I'm criticizing the approach of starting the analysis in a vacuum and coming up with a hypothesis that fits the abstract pattern, when simple domain knowledge (from looking at a website, or a Caltrain station notice board) would have put them on the "right track" from the start.
What the authors did falls into the trap of a very stereotypical criticism of data science and doesn't do data science any favors.
What the authors did falls into the trap of a very stereotypical criticism of data science and doesn't do data science any favors.