For this particular example, I don’t have to go very far to find people who disagree with my aesthetic. My spouse is also a statistician, and we sometimes have this same disagreement!

]]>I hope that the freelance work is still keeping you busy. ]]>

Our discussion, resulted in some hypothetical questions:

1. How does the Nigerian statistical aesthetic apply when there are many more graphs (e.g. 77 million)?

2. How about when there is a more sophisticated statistical analysis?

– LISA Collaborators Weekly Meeting Group

]]>And having all the data might provide truth but is that truth of value? I interviewed for a project at a big pharma company (unfortunately it was killed the next week). Since it was in biologics manufacturing, a few big data companies had their hands in all the process data. But the engineer said to me ‘You are so different from all of them. You think about the content. These analysts bring me graphs and say things like feed amount is correlated with microbe count.’ That is true but useless. And adding terms to this model which Mark van de Laan disparages offers a chance to segment the data and dive deeper for real world adjustments to explanations. And as Deming pointed out and the ASA sued (but lost), sometimes sampling is better than full enumeration.

]]>Modeling provides a language for scientific discourse. Models help clarify our process of reasoning. They sharpen our ken. Ideally, they make explicit the assumptions that are otherwise unstated or hidden in scientific discourse. As our understanding, assumptions, and goals change, so should the models. The dichotomy of models as right and wrong is a gross misrepresentation of the richness and dynamism of the modeling process.

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