High-productivity through interactivity
Laws of data driven decision making
I came across a wonderful, somewhat tongue in cheek, posting by Edmund Freeman here. Anyone that has been asked to do an analysis to support decision making will recognize these principles. Here they go:
Certainty Principle: Certainty is inversely proportional to knowledge
Anyone that works with data will understand the limitations of data and any inference analysis or signal processing. When making statements based on inference, the proper caveats will be mentioned. Anyone that presents results from data analysis as straightforward and true is not hindered by any knowledge.
Bad Analysis Law: bad analysis drives out good analysis
Bad analysis invariably conforms to people's pre-conceived notions, so they like hearing it. It's also 100% certain in it's results, no caveats, nothing hard to understand, and usually gets produced first. This means that good analysis always has an uphill fight.
Certainty Principle: Certainty is inversely proportional to knowledge
Anyone that works with data will understand the limitations of data and any inference analysis or signal processing. When making statements based on inference, the proper caveats will be mentioned. Anyone that presents results from data analysis as straightforward and true is not hindered by any knowledge.
Bad Analysis Law: bad analysis drives out good analysis
Bad analysis invariably conforms to people's pre-conceived notions, so they like hearing it. It's also 100% certain in it's results, no caveats, nothing hard to understand, and usually gets produced first. This means that good analysis always has an uphill fight.
Add a Comment
About
Badge
© 2009 Created by Theodore Omtzigt on Ning. Create Your Own Social Network
You need to be a member of Computational Science and Engineering to add comments!
Join this social network