Rules Based Analytics
Rules based analytics
Select a law to read the author’s explanation.
laws
Sturgeon’s Law
90% of everything is crap
This is the primary, dominant and most significant rule when you work with data. Crap finds its way into very many statistics that we rely on to frame our understanding of the world.
Twyman’s Law
Any figure that looks interesting or different is usually wrong
A sibling heuristic of Sturgeon, and one that is particularly important for analysts wishing to impress managers or clients with dramatic new findings or groundbreaking analysis.
Segal’s Law
If you have one clock, you know the time. If you have two, you are not sure
Because of Sturgeon, it is always good to find triangulating data. More often this additional data will not triangulate. You have been Segal’ed.
Goodhart’s Law
When a measure becomes a target, it ceases to be a good measure
Goodhart’s law explains Sturgeon in some contexts. For instance, when you are working with KPIs produced by a bureaucrat whose incentives (financial or otherwise) are linked to that set of KPIs you should be very wary.
Box’s Law
All models are wrong, but some are useful
What makes Box true is the process of building the model – identifying the parameters, looking for the data and most importantly, running the scenarios and sensitivities.
Not quite Socrates’ Law
I am the wisest man alive, for I know one thing, and that is that I know nothing
The way to deal with this is to argue. We want many views because if we have one view when everyone knows nothing, we should be very afraid.
An exploration of classic analytics “laws”, exposing why data misleads, models deceive, KPIs fail, and intellectual humility, debate, and skepticism matter more than polished charts.
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