Posted on 04/17/2019 11:40:49 AM PDT by Heartlander
I still remember my prof in Statistics 101:
Statistics NEVER give you an answer, at best, they give you another question.
This example is why so many ‘scientist study shows’ papers cannot be reproduced. The data is ‘interpreted’ to get the result that the researcher wants. Further, often the question is framed so that the results are predetermined prior to the data being obtained.
Raw data that may invalidate the results need not apply.
And as much as they try to tell you otherwise... all “AI/Machine Learning” is, is glorified statistical analysis... which is why a woman was run over by a car... because as much as they try to tell you otherwise, computers are dumb.
I remember Global Warmists argued that hurricanes were getting worse because of the number of people impacted by them. But they ignored the fact that one hurricane hitting a major population center will shoe more people impacted than several WORSE hurricanes in unpopulated areas.
The dots seem to form a kitty, Doctor.
Plus the internet is full of shitposters who are deliberately flooding Big Data with garbage, sarcasm, trolling, and other things not easily detected by algorithm.
My engineering prof:
Figures can’t lie, but liars can figure.
State universities LIVE on this. Only an average of 18 students per class... but 90% of your classes are lecture-center classes with 300 students!
... that’s because “average class size” is reported from the point of view of the class, not the student. So, yes, there are loads of classes with less than 10 students... but your typical student isn’t going to be in them. Consider a university with 100 classes: 90 have 9 students, and 10 have 219 students. From the point of view of the class, 90% of the classes have less than 10 students, but from the point of view of the student. That’s the way the state universities frame it. But from the point of view of the student, more than 70% if the classes have more than 200 students.
I did a research paper for my MBA on cluster analysis. I took all privately owned sports teams and clustered them. I used Yes/No criteria to minimize subjectivity and bias. Twenty one variables included bought or inherited team, championship or not, wealth came from manufacturing or service industries, etc.
I got six clusters that indicated that owners who got their wealth from manufacturing and bought the team were most successful.
A similar study for bank profitability showed little clustering based on bank deposits, office space, age or other variables.
Good example:
My daughter is a Registered Nurse. She works in a hospital.
While a teenager she worked at a local restaurant called Eat n’ Park. That is a Pittsburgh area chain. The company that owns it is called The Eat n’ Park Hospitality Group.
My daughter has not lived at home in a decade. Despite this I get fistfuls of junk mail trying to sell computers and things addressed to my daughter, at the “Eat n’ Park Hospital Group”, which is apparently headquartered in my modest house in suburban Pittsburgh.
Data mining and AI obviously put some pieces together in a very incorrect way.
Reminds me of Jim Bouton in Ball Four when he was in contract negotiations and management was listing all of the “bad” statistics from the prior year. His response was “Tell Your Statistics to Shut up!”.
Always loved that one!
It always gets back to the base rate.....
Sounds like it's time to buy a slicer and a case of Kaiser rolls and put in a drive thru window. The sky's the limit!
at the end of the day, computers only do what you tell them to do... thus inherent biased
Its not simply inherently biased, its inherently stupid.
ML can bring you some great insights, but it, like everything else is simply a tool. The Bell Curve exists even within these systems... and unlike a human interaction where you can tell by mannerisms, history of that person, etc.. that they are outside what the algorithm picks up, because they are on the edges of the bell curve, a human can pick up.
This is why an autonomous vehicle that physically “saw” a person crossing the street at night, long before a human could have possible saw them, decided to drive right over her, while other cars simply went around her.
If you divide data into arbitrary groups one can get whatever interpretation one wishes.
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