**Features**are qualitative because you have to

**count**them. Data are quantitative because it are numbers themselves. The shift from an equivalence-class and a variable is exactly the shift from counting to inference. What is done into statistics is the featuring of equivalence-classes. We take a difference into account, let say we say that this is different from that. That is something different. Now can the difference be made between hard and soft science by the difference of quantitative or qualitative. There is although only one value when variability is active. You have to say if it is good or bad following a certain hypothesis.

When we take the effective we gonna use classes, when we don't use the effective we gonna look for differences. All differences together form the effective. The effective (the sum of the absolute frequencies) is scientific observative. Which means the making of classes. The relative frequency at the other hand is rather democratic and means the application of classes. Therefore we call it inference, everything becomes digitalized in use. It is a sort of percentage and there is a reduction to soft science. Cumulative functions are also never monotone increasing. Classes are presented by the middle of the class. Thereafter are parameters in use which are featuring values. The median takes the class-length into account the modus or the mean the class. This last section takes with many mathematics and not really statistics to the aspiration of hard science. This forms without more rather a problem with the median because of its dependence to the class itself and not reality as a context.

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