Hi, I seem to remember from classes that one effect of scaling / standardising data was to get better results in any analysis. But what I'm seeing when I study various explanations on scaling is that we get exactly the same results, just that when we look at standardised data it's easier to see proportionate effects. This is all very well for the data scientist to further investigate, but from a practical point of view, (especially IF it doesn't improve the accuracy of the result) surely it adds complication to 'telling the story' of the model to non-DS people? So, is scaling a technique for the DS to use to find effects, while eventually delivering a non-scaled version to the users? I'd like to be able to give the true story to my students, not some fairy story based on my misunderstanding. Hope you can help with this. Michael
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