inference
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- University of Toronto - Department of Statistical Sciences - On Some Principles of Statistical Inference
- University of Wisconsin Pressbooks - Process of Science Companion: Data Analysis, Statistics and Experimental Design - Statistical Inference � Basic Concepts
- National Center for Biotechnology Information - PubMed Central - Statistical inference through estimation: Recommendations from the International Society of Physiotherapy Journal Editors
- OpenStax - Principles of Data Science - Statistical Inference and Confidence Intervals
- University of Bristol - 4 Ideas of statistical inference
- Corporate Finance Institute - Inferential Statistics
- Statistics LibreTexts - Foundations for Inference
inference, in statistics, the process of drawing conclusions about a parameter one is seeking to measure or estimate. Often scientists have many measurements of an object—say, the mass of an electron—and wish to choose the best measure. One principal approach of statistical inference is Bayesian estimation, which incorporates reasonable expectations or prior judgments (perhaps based on previous studies), as well as new observations or experimental results. Another method is the likelihood approach, in which “prior probabilities” are eschewed in favour of calculating a value of the parameter that would be most “likely” to produce the observed distribution of experimental outcomes.
In parametric inference, a particular mathematical form of the distribution function is assumed. Nonparametric inference avoids this assumption and is used to estimate parameter values of an unknown distribution having an unknown functional form.