Statistical inference | Wikipedia audio article
Channel: wikipedia tts
Date: 2019-10-05

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Statistical_inference


00:00:39 1 Introduction
00:02:08 2 Models and assumptions
00:02:45 2.1 Degree of models/assumptions
00:04:47 2.2 Importance of valid models/assumptions
00:06:28 2.2.1 Approximate distributions
00:08:47 2.3 Randomization-based models
00:11:03 2.3.1 Model-based analysis of randomized experiments
00:11:58 2.3.2 Model-free randomization inference
00:15:55 3 Paradigms for inference
00:16:39 3.1 Frequentist inference
00:17:13 3.1.1 Examples of frequentist inference
00:17:27 3.1.2 Frequentist inference, objectivity, and decision theory
00:19:47 3.2 Bayesian inference
00:20:19 3.2.1 Examples of Bayesian inference
00:20:35 3.2.2 Bayesian inference, subjectivity and decision theory
00:22:22 3.3 Likelihood-based inference
00:22:52 3.4 AIC-based inference
00:23:43 3.5 Other paradigms for inference
00:23:53 3.5.1 Minimum description length
00:25:28 3.5.2 Fiducial inference
00:26:22 3.5.3 Structural inference
00:26:59 4 Inference topics
00:27:39 5 History
00:28:02 6 See also



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SUMMARY
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Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.  It is assumed that the observed data set is sampled from a larger population.
Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.