D.1. The Non-Uniformity: The causes active in one sample do not necessarily repeat, or change, in each next sample set of observations.
E.1. The empirical observations are not governed by the uniform causations as in theory. If at one time the price had risen because demand had risen, another time it may be due to a taxation, another time against same demand a short supply of raw materials may trigger rise in price, or another time a negative technological shock (say flood) may reduce the supply against same demand, or what and what not. Moreover, there are factors beyond observation, or factors which may be specific to a particular data set (a country, a time etc). All of these imply that data generated by real life events is a large uncontrolled experiment data and to ignore it would be erroneous.
D.2. The Non-Generality: The mathematical functions cannot yield meaningful results when data they use is generated by heterogenous, sometimes irrelevant, circumstances.
E.2. Neither are the empirical observations governed by the uniform causations, nor is it wise to add up all the heterogenous events without due care of their heterogenous nature. Two countries with different degrees of development may face business cycle but in each case it would be driven by some overlapping but also other substantially different set of causal factors. And to average them together would not be informative at all. Thus, mathematical generalization, say averages, using a data generated by heterogenous, or irrelevant, circumstances is not meaningful. Simply, irrelevant cannot be combined mathematically to yield meaningful results.