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2021
In this final chapter we demonstrate some of the under-appreciated non-parametric tests. These are the cousins (not siblings) of the parametric ones. For example, if your dataset does not meet the assumptions for the t-test (parametric), then Mann-Whitney test (non-parametric) in this section can be an alternative for you.
Saiyidi Mat Roni +1 more
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In this final chapter we demonstrate some of the under-appreciated non-parametric tests. These are the cousins (not siblings) of the parametric ones. For example, if your dataset does not meet the assumptions for the t-test (parametric), then Mann-Whitney test (non-parametric) in this section can be an alternative for you.
Saiyidi Mat Roni +1 more
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2011
The t-tests reviewed in the previous chapter are suitable for studies with normally distributed results. However, if there are outliers, then the t-tests are not sensitive and non-parametric tests have to be applied. We should add that non-parametric are also adequate for testing normally distributed data. And, so, these tests are, actually, universal,
Ton J. Cleophas, Aeilko H. Zwinderman
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The t-tests reviewed in the previous chapter are suitable for studies with normally distributed results. However, if there are outliers, then the t-tests are not sensitive and non-parametric tests have to be applied. We should add that non-parametric are also adequate for testing normally distributed data. And, so, these tests are, actually, universal,
Ton J. Cleophas, Aeilko H. Zwinderman
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1972
In the last two chapters we have seen how the t-test can be used to derive confidence intervals and significance tests for an unknown mean. In this chapter we present some alternative methods of analysis which do not make the assumption that the observations are normally distributed.
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In the last two chapters we have seen how the t-test can be used to derive confidence intervals and significance tests for an unknown mean. In this chapter we present some alternative methods of analysis which do not make the assumption that the observations are normally distributed.
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2015
In parametric tests we generally assume a particular form of the population distribution (say, normal distribution) from which a random sample is drawn and we try to construct a test criterion (for testing hypothesis regarding parameter of the population) and the distribution of the test criterion depends upon the parent population.
Pradip Kumar Sahu +2 more
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In parametric tests we generally assume a particular form of the population distribution (say, normal distribution) from which a random sample is drawn and we try to construct a test criterion (for testing hypothesis regarding parameter of the population) and the distribution of the test criterion depends upon the parent population.
Pradip Kumar Sahu +2 more
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2000
Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
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Parametric tests require some specific conditions about the distributions of scores in the populations of interest. When these conditions cannot be formally tested, researchers assume that they exist. The interpretation of the results derived from parametric tests relies heavily on these requirements not being seriously violated. When these assumptions
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SPBS: Programs for non-parametric tests
Computer Programs in Biomedicine, 1983Abstract A new series of programs for non-parametric tests have been inserted in SPBS, a statistical package for biological sciences that applies biostatistical methods using microcomputer software [Comput. Prog. Biomed. 14 (1982) 7–20]. Programs presented here cover non-parametric tests for multiple comparisons between two or more groups of paired ...
A, Giannangeli, M, Recchia, M, Rocchetti
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Non-Parametric Statistical Tests
2018Chapter 10 introduces the most commonly used non-parametric tests, as well as the appropriate situations for their use. It also examines concepts of sample size, power, and validity when using these tests. In addition, this chapter discusses the major advantages and disadvantages of non-parametric tests, and how they compare to their optimal parametric
Felipe Fregni +2 more
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