Parametric Tests
     Based on distributions
     Parameters are used (mean and standard 
     deviations)
     T-test 
     ANOVA etc.
     Aim of Non Parametric 
     Tests
      Nonparametric,  or  distribution  free  tests  are  so-called 
       because the assumptions underlying their use are “fewer 
       and weaker than those associated with parametric tests” 
       (Siegel & Castellan, 1988, p. 34). 
     To put it another way, nonparametric tests require few if any
      assumptions about the shapes of the underlying population 
       distributions. For this reason, they are often used in place of 
       parametric tests if/when one feels that the assumptions of 
       the parametric test have been too grossly violated (e.g., if 
       the distributions are too severely skewed).
      For example non parametric is used when either assumption 
       of normality or homogeneity of variances is violated.
      Non Parametric tests are often called distribution free.
                                Parametric            Non-parametric
    Assumed distribution           Normal                   Any
      Assumed variance         Homogeneous                  Any
         Typical data          Ratio or Interval     Ordinal or Nominal
    Data set relationships      Independent                 Any
    Usual central measure           Mean                   Median
          Benefits             Can draw more      Simplicity; Less affected 
                                 conclusions             by outliers
            Tests                                              
          Choosing         Choosing parametric tes Choosing a non-parametr
       Correlation test    t      Pearson         ic test Spearman
   Independent measures, Independent-measures t      Mann-Whitney test
          2 groups         -test
                                       
   Independent measures,  One-way, independent-      Kruskal-Wallis test
         >2 groups            measures ANOVA
    Repeated measures, 2     Matched-pair t-test        Wilcoxon test
         conditions
   Repeated measures, >2     One-way, repeated        Friedman's  test
         conditions           measures ANOVA
     Runs Test of Randomness 
      It is used to know the randomness in data. Run test of randomness 
       is sometimes called the Geary test, and it is a nonparametric test. 
      Run  test  of  randomness  is  an  alternative  test  to  test 
       autocorrelation in the data. Autocorrelation means that the data 
       has correlation with its lagged value. To confirm whether or not the 
       data has correlation with the lagged value, run test of randomness 
       is applied. 
      In the stock market, run test of randomness is applied to know if 
       the stock price of a particular company is behaving randomly, or if 
       there is any pattern. 
      Run  test  of  randomness  is  basically  based  on  the  run.  Run  is 
       basically a sequence of one symbol such as + or -. 
      Run test of randomness assumes that the mean and variance are 
       constant       and      the     probability     is     independent.
     Hypothesis: Null hypothesis assumes that 
      the sample is random against alternative 
      that sample is not random