> The point of rigour is not to destroy all intuition; instead, it should be used to destroy bad intuition while clarifying and elevating good intuition.
This is a key insight; it's something I've struggled to communicate in a software engineering setting, or in entrepreneurial settings.
It's easy to get stuck in the "data driven" mindset, as if data was the be-all and end-all, and not just a stepping stone towards an ever more refined mental model. I think of "data" akin to the second phase in TFA (the "rigor" phase). It is necessary to think in a grounded, empirical way, but it is also a shame to be straight-jacketed by unsafe extrapolations from the data.
> It's easy to get stuck in the "data driven" mindset, as if data was the be-all and end-all, and not just a stepping stone towards an ever more refined mental model.
Yes. "Data driven" either includes sound statistical modelling and inference, or is just a thiny veiled information bias.
rigour is is not about destroying bad intuition, but rather formalizing good intuition, imho. The ability to know good from bad is somewhere in-between total newb and expert.
This is a key insight; it's something I've struggled to communicate in a software engineering setting, or in entrepreneurial settings.
It's easy to get stuck in the "data driven" mindset, as if data was the be-all and end-all, and not just a stepping stone towards an ever more refined mental model. I think of "data" akin to the second phase in TFA (the "rigor" phase). It is necessary to think in a grounded, empirical way, but it is also a shame to be straight-jacketed by unsafe extrapolations from the data.