Philosophy of statistics

The philosophy of statistics involves the meaning, justification, utility, use and abuse of Statistics and its methodology, and ethical and epistemological issues involved in the consideration of choice and interpretation of data and methods of Statistics.

Foundations of statistics involves issues in theoretical statistics, its as goals and optimization methods to meet these goals, parametric assumptions or lack thereof considered in Nonparametric statistics, model selection for the underlying distribution, and interpretation of the meaning of inferences made using Statistics, related to the philosophy of probability and the philosophy of science. Discussion of the selection of the goals and the meaning of optimization, in foundations of statitistcs, are the subject of the philosophy of statistics. Selection of distribution models, and of the means of selection, is the subject of the philosophy of statitistics, whereas the mathemtatics of optimization is the subject of nonparametric statistics.

Issues arise involving sample size, such as cost and efficiency, are common, such as in polling and pharmeceutical research.

Extra-mathematical considerations in the design of experiments and accomodating these issues arise in most actual experiments..

The motivation and justificaton of Data Analysis and Experimental Design, as part of the Scientific Method are considered.

Distinctions between induction and logical deduction relevant to inferences from data and evidence arise, such as in frequentist interpretatoins are compared with degrees of certainty derived from Bayesian inference.

Issues in the philospohy of statistics arise through out the history of statistics. Causality considerations arise with interpretations of, and definitions of, correlation, and in the theory of measurement.

Ethics associated with epistemology and medical applications arise from potential abuse of statistics, such as selection of method or transformations of the data to arrive at different probability conclusions for the same data set. For example, the meaning of applications of a statistical inference to a single person, such as one single cancer patient, when there is no frequentist interpretation for that patient to adopt.

Further reading