2019•Cambridge University Press eBooksRequires access

Using Non-Parametric Tests

Paul A. Cairns

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Abstract

Non-parametric tests, in particular rank-based tests, are often proposed as robust alternatives to parametric tests like t -tests when the assumptions of parametric tests are violated. However, non-parametric tests have their own assumptions which, when not considered, can lead to misinterpretation and unsound conclusions based on those tests. This chapter explores these problems and differentiates between the more and less robust non-parametric tests. Modern robust alternative non-parametric tests are suggested to replace the less robust tests.

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What this paper is about

Non-parametric tests, in particular rank-based tests, are often proposed as robust alternatives to parametric tests like t -tests when the assumptions of parametric tests are violated. However, non-parametric tests have their own assumptions which, when not considered, can lead to misinterpretation and unsound conclusions based on those tests. This chapter explores these problems and differentiates between the more and less robust non-parametric tests. Modern robust alternative non-parametric tests are suggested to replace the less robust tests.

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Available abstract

Non-parametric tests, in particular rank-based tests, are often proposed as robust alternatives to parametric tests like t -tests when the assumptions of parametric tests are violated. However, non-parametric tests have their own assumptions which, when not considered, can lead to misinterpretation and unsound conclusions based on those tests. This chapter explores these problems and differentiates between the more and less robust non-parametric tests. Modern robust alternative non-parametric tests are suggested to replace the less robust tests.

Key concepts: Parametric statistics, Parametric model, Rank (graph theory), Nonparametric statistics, Computer science, Econometrics, Mathematics, Statistics

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