A related question: should general linear models (for statistical inference) still be taught? Shouldn't the curriculum focus on sophisticated computer-intensive simulations to conpute confidence intervals and for statistical analyses in general?
No understanding either
The advantages are:
* easy to code, no use of black box software anymore
Do you really think simulation software is a lucidly clear transparent box?
* data driven, model-free, non parametric approach, applies to any type of data
There is a bizarre notion around that simulations that use the obtained data are unbiassed. This is NONSENSE, they are biassed by the sample received. E.g. If 1 group happens to have an outlier it will increase the se as it moves from group in the random simulation
Emeritus Professor Diana Kornbrot
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School of Psychology
University of Hertfordshire
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