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On a Non-parametric Test in Life TestSome of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +1 more source