Daniela Fonseca
Nicole Leithof
Amarillis Rubido
Samantha Tang
Ricardo Uzcátegui
Tuesday, October 15, 2024
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Thursday, October 3, 2024
La invasión del "fake" research (o por qué debemos considerar lo "fake" como una parte del todo)
Climate scientist admits to overhyping research to get published (in the Telegraph)
Are you surprised? 😂
Let's do a bit of Philosophy of statistics.
I remember Professor Barr's adage in my Statistical Analysis class: if you find two compelling reasons for a mistake (outside the research), it's not a mistake.
What an error in statistics?
Observational error (or Measurement error) is the difference between a measured value of a quantity and its true value. In statistics, an error is not necessarily a "mistake."
The reason is that variability is an inherent part of the results of measurement processes.
Measurement errors can be divided into two: random and systematic.
Random errors are errors in measurement that lead to inconsistent measurable values when repeated measurements of a constant attribute or quantity are taken (errors can get repeated even if looking for proof)
Systematic errors are not determined by chance but are introduced by repeatable processes inherent to the system.
Put differently, the house would lose if randomness was true.
A systematic error is not determined by chance but by a repeatable process inherent to the system. In gambling parlance, Casino bias is part of the game; otherwise, the House loses!
Now, is complete randomness REAL? Some think not.
If not, how do you argue?
