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A common mistake technical people make is to be too theoretical or overcomplicated with their work and decision making, where everything has to be some math mod
by hntrader 6y ago
A common mistake technical people make is to be too theoretical or overcomplicated with their work and decision making, where everything has to be some math model written out in a nicely formatted LaTeX document. Don't fall into that trap, it is very ineffective.
Stats is a tool like any other tool. Boil your question down to the fundamentals (first principles) and maybe stats is one of the tools you decide to use to solve part of it, where appropriate.
Most questions involving data can be answered with something as basic as a plot.
- cambalache 6y agoEasier said than done. The complete history of science is the human struggle to boil down the mathematical modelling of nature to its fundamentals.
- hntrader 6y ago"mathematical modelling of nature" My point is most real world problems people face (even most problems that data scientists face at work) shouldn't be modelled with formal math. Basic reasoning, tinkering, plotting data and playing around with data in Excel or Python is usually sufficient to answer most questions, yet many will try to overcomplicate the issue with complex math or stats. Perhaps in an attempt to impress their peers, or perhaps because they've just come out of years of university that taught them to think theoretically. I'm reminded of that meme going around. 70 IQ: plot data 100 IQ: support vector machines! deep Q-nets! 130 IQ: plot data