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A theory of statistical inference was developed by Charles S. Peirce in "Illustrations of the Logic of Science" (1877–1878) and "A Theory of Probable Inference" (1883), two publications that emphasized the importance of randomization-based inference in statistics.
Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.
Statistical hypothesis testing plays an important role in the whole of statistics and in statistical inference. For example, Lehmann (1992) in a review of the fundamental paper by Neyman and Pearson (1933) says: "Nevertheless, despite their shortcomings, the new paradigm formulated in the 1933 paper, and the many developments carried out within ...
He wrote a book entitled Manuscript on Deciphering Cryptographic Messages, containing detailed discussions on statistics and cryptanalysis. [2] [3] [4] Al-Kindi also made the earliest known use of statistical inference. [1] 13th century – An important contribution of Ibn Adlan was on sample size for use of frequency analysis. [1]
Publications by Fisher, like "Statistical Methods for Research Workers" in 1925 and "The Design of Experiments" in 1935, [8] contributed to the popularity of significance testing, which is a probabilistic approach to deductive inference.
The theory of statistics provides a basis for the whole range of techniques, in both study design and data analysis, that are used within applications of statistics. [1] [2] The theory covers approaches to statistical-decision problems and to statistical inference, and the actions and deductions that satisfy the basic principles stated for these different approaches.
Particularly when the frequency interpretation of probability is mistakenly assumed to be the only possible basis for frequentist inference. So, for example, a list of mis-interpretations of the meaning of p-values accompanies the article on p-values; controversies are detailed in the article on statistical hypothesis testing.
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. Campaigns for statistical literacy must wrestle with the problem that most interesting questions around individual risk are very difficult to determine ...