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Epistemology of Statistics
Andrius: We are collecting and systematizing the ways of figuring things out in statistics. Thank you for helping us!
The insights we gain may, in general, help us understand the assumptions that experimental science depends upon, and in particular, help us interpret and analyze the Raudys hierarchy of as expressing how a single layer perceptron grows in conceptual sophistication.
Sources of Examples
Anderson, Norman (2001). Empirical Direction in Design and Analysis.
Collection of Examples
Andrius: I am starting by collecting very basic examples inspired by the Wikipedia article.
Defining a population
Simply defining a population gives us a first step in formulating what we are interested in.
Classification
Distinguishing categories amongst a population.
Ranking
Ordering elements. Determining the median and percentiles.
Measuring, adding, substracting using an
As with temperature on a Fahrenheit scale. Addition and substraction may be meaningful but there is no meaningful zero with regard to which multiplication or division could be performed.
Measuring, multiplying, dividing, using a
Where zero is meaningful, and thus multiplication and division of values is meaningful.
Sampling
Establishing and characterizing a representative sample allows us to draw conclusions about the entire population.
Averaging
Averaging reduces a population to a single, typical individual.
Deviation
Deviation allows us to consider the extent to which a particular individual resembles or not a typical individual.
Bounding errors
Calculating error bounds distinguishes what is believable from what is not.
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