So based on that everything is inconclusive? That’s good enough for me. The data doesn’t lie but has definitely been misinterpreted by Fizzer.
In a sense, yes. While the data may not be inconclusive, and may or may not show a strong effect, everyone can just make their own narrative about that: "It's because of the boot limit change" vs. "It's because booted people get removed" vs. "It's because of people being more enthusiastic" vs. "It's because of the new rating system" vs. "It's because of the people playing this template being more used to lower boot times" vs. "It's because it was bad weather outside so most people had more time to play" etc.
None of that could be proven nor disproven with the way that the data is structured, so you risk that everyone just sticks to their entrenched opinion no matter what. Very often, collecting and analysing data is useful, but there are times, sadly like this, that any data-analysis is dead in the water before even starting it.
Has Fizzer misinterpreted the data? Very likely, if this was an analysis in a scientific paper and I was the (statistical) reviewer, I would reject it immediately. But we likely cannot proof that he misinterpreted it either.
Technical note: some things may be possible, but it would require an extensive data collection process, including getting the reasons for boots before and after, modelling if people would have booted again if they weren't removed and/or discarding boots of people who would have been removed, and correcting for a bunch of other variables to the extend possible, for example through matching or propensity score matching. The time and effort required versus the small chance of being successful and the limited impact that success could have should disqualify such a study immediately.
However, it's your time that you invest, I'm just trying to warn everyone here to not expect too much from it in this case.
