Yes, this is pretty much just pseudo-statistical nonsense. Its like it is taken right from the playbook of Trump’s election denial or a fringe Climate change denial. The graphs show limited data which doesn’t actually indicated what they say it indicates. Speculations are drawn without any justification for doing so, and the conclusion has nothing to do with presiding arguments raised.
First of all it looks at suspiciously limited data (Oct 26th to Nov 11th) when we know more data is available. This is a known tactic from climate deniers who claim e.g. that data from a single weather station in Canada from October 2006 to June 2014 shows the world is actually cooling.
Second of all it makes vague assumptions about distributions without clarifying or even justifying why it should have a different distribution. This is straight from the playbook of Trump’s election denialism where some variation of election results was somehow proof of tampering[1].
With statistics you can find any results you want if you look at limited enough data from a large enough pool, and you can find any abnormality you want by giving unrealistic expectations. This is why you need to clarify which distribution you expect, and—more importantly—justify it by pointing at other examples from similar pools. This is never done here, which is why I call it pseudo-statistical nonesence. Stating it is abnormal is not enough, even when you provide some fancy box plots to do so.
Yes, this is pretty much just pseudo-statistical nonsense. Its like it is taken right from the playbook of Trump’s election denial or a fringe Climate change denial. The graphs show limited data which doesn’t actually indicated what they say it indicates. Speculations are drawn without any justification for doing so, and the conclusion has nothing to do with presiding arguments raised.
I think it raises valid questions, can you elaborate what's wrong with the article?
First of all it looks at suspiciously limited data (Oct 26th to Nov 11th) when we know more data is available. This is a known tactic from climate deniers who claim e.g. that data from a single weather station in Canada from October 2006 to June 2014 shows the world is actually cooling.
Second of all it makes vague assumptions about distributions without clarifying or even justifying why it should have a different distribution. This is straight from the playbook of Trump’s election denialism where some variation of election results was somehow proof of tampering[1].
With statistics you can find any results you want if you look at limited enough data from a large enough pool, and you can find any abnormality you want by giving unrealistic expectations. This is why you need to clarify which distribution you expect, and—more importantly—justify it by pointing at other examples from similar pools. This is never done here, which is why I call it pseudo-statistical nonesence. Stating it is abnormal is not enough, even when you provide some fancy box plots to do so.
1: https://www.youtube.com/watch?v=etx0k1nLn78
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