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I am forced to get famiilar with this topic quickly and I am struggling with the following after reading it in a paper. Can someone help with the underlined ones. I also have a follow up question which I will introduce after this.

From the paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC153434/, i saw the following and wanted to get clear.

Advantages of nonparametric methods

Nonparametric methods require no or very limited assumptions to be made about the format of the data, and they may therefore be preferable when the assumptions required for parametric methods are not valid.

Nonparametric methods can be useful for dealing with unexpected, outlying observations that might be problematic with a parametric approach.

Nonparametric methods are intuitive and are simple to carry out by hand, for small samples at least.

Nonparametric methods are often useful in the analysis of ordered categorical data in which assignation of scores to individual categories may be inappropriate. For example, non-parametric methods can be used to analyse alcohol consumption directly using the categories never, a few times per year, monthly, weekly, a few times per week, daily and a few times per day. In contrast, parametric methods require scores (i.e. 1–7) to be assigned to each category, with the implicit assumption that the effect of moving from one category to the next is fixed.

Disadvantages of nonparametric methods

Nonparametric methods may lack power as compared with more traditional approaches.This is a particular concern if the sample size is small or if the assumptions for the corresponding parametric method (e.g. Normality of the data) hold.

Nonparametric methods are geared toward hypothesis testing rather than estimation of effects.It is often possible to obtain nonparametric estimates and associated confidence intervals, but this is not generally straightforward.

Tied values can be problematic when these are common, and adjustments to the test statistic may be necessary.

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# Non-parametric regression

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