Statistical Methods are widely used in Medical, Biological, Clinical, Business and Engineering field. The data which form the basis for the statistical methods helps us to take scientific and informed decisions. Statistical methods deal with the collection, compilation, analysis and making inference from the data.
The book mainly focuses on non-parametric aspects of Statistical methods. Nonparametric methods or tests are used when the assumption about the distribution of the variables in the data set is not known or does not follow normal distribution assumption. Nonparametric methods are useful to deal with ordered categorical data. When the sample size is large, statistical tests are robust due to the central limit theorem property. When sample size is small one need to use non-parametric tests. Compared to parametric tests, non-parametric tests are less powerful i.e., if we fail to reject the null hypothesis even if it is false. When the data set involves ranks or measured in ordinal scale then non-parametric tests are useful and easy to construct than parametric tests.
The book uses open-source R statistical software to carry out different non-parametric statistical methods with sample datasets.
