A monotonic transformation refers to a mathematical function that preserves the order of the values it transforms, meaning if one value is greater than another in the original dataset, it remains greater after transformation. This concept is crucial when discussing transformations of random variables, as it allows for certain properties, like distributions and expectations, to be maintained under specific functions, thus ensuring that probabilistic interpretations remain valid even after altering the scale or units of measurement.
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