how the nonparametric method works
parametric and nonparametric methods are often used on different types of data parametric statistics generally require interval or ratio data an example of this type of data is age income height and weight in which the values are continuous and the intervals between values have meaning in contrast nonparametric statistics are typically used on data that nominal or ordinal nominal variables are variables for which the values have not quantitative value common nominal variables in social science research for example include sex whose possible values are discrete categories male and female other common nominal variables in social science research are race marital status educational level and employment status employed versus unemployed ordinal variables are those in which the value suggests some order an example of an ordinal variable would be if a survey respondent asked on a scale of 1 to 5 with 1 being extremely dissatisfied and 5 being extremely satisfied how would you rate your experience with the cable company parametric statistics may too be applied to populations with other known distribution types however nonparametric statistics do not require that the population data meet the assumptions required for parametric statistics nonparametric statistics therefore fall into a category of statistics sometimes referred to as distribution free often nonparametric methods will be used when the population data has an unknown distribution or when the sample size is small special considerationsalthough nonparametric statistics have the advantage of having to meet few assumptions they are less powerful than parametric statistics this means that they may not show a relationship between two variables when in fact one exists nonparametric statistics have gained appreciation due to their ease of use as the need for parameters is relieved the data becomes more applicable to a larger variety of tests this type of statistics can be used without the mean sample size standard deviation or the estimation of any other related parameters when none of that information is available since nonparametric statistics makes fewer assumptions about the sample data its application is wider in scope than parametric statistics in cases where parametric testing is more appropriate nonparametric methods will be less efficient this is because nonparametric statistics discard some information that is available in the data unlike parametric statistics common nonparametric tests include chi square wilcoxon rank sum test kruskal wallis test and spearman s rank order correlation examples of the nonparametric methodconsider a financial analyst who wishes to estimate the value at risk var of an investment the analyst gathers earnings data from hundreds of similar investments over a similar time horizon rather than assume that the earnings follow a normal distribution she uses the histogram to estimate the distribution nonparametrically the 5th percentile of this histogram then provides the analyst with a nonparametric estimate of var for a second example consider a different researcher who wants to know whether average hours of sleep are linked to how frequently one falls ill because many people get sick rarely if at all and occasional others get sick far more often than most others the distribution of illness frequency is clearly non normal being right skewed and outlier prone thus rather than use a method that assumes a normal distribution for illness frequency as is done in classical regression analysis for example the researcher decides to use a nonparametric method such as quantile regression analysis 