DC-Den

Previously I showed one-way  ANOVA  for comparing samples means. It assumes the samples are normally distributed. For non-parametric samples, we have Kruskal-Wallis test. Kruskal-Wallis the alternative to one-way ANOVA when your samples do not follow the normal distribution. Distribution 2 samples > 2 samples Normal Two Sample Mean Test ANOVA Non-parametric Mann-Whitney Kruskal-Wallis How does Kruskal-Wallis test work? Kruskal-Wallis test is used to test if there is any statistically significant differences between the medians of three or more independent (unrelated) groups. It is similar to Mann-Whitney test in that it sorts the data altogether, ranks them and then calculates the test statistics. But where Mann-Whitney uses NORM.S.DIST to calculate the test statistics, Kruskal-Wallis uses CHISQ.DIST instead. Implementing Kruskal Wallis in LAMBDA Step 1 : Group data together We can group the data together using

The Mann-Whitney Test Mann-Whitney test helps you compare two sets of data when they are not normally distributed. I would use Mann-Whitney test only after I confirm using the  Anderson-Darling test . I should add that I run a box-plot first before running the AD test. I used to think that Mann-Whitney compares the medians of two data sets. But in the process of implementing Mann-Whitney in Excel LAMBDA, I found out that Mann-Whitney test compares the mean ranks, in doing so, it determines if the two data set came from the same population. Step 1: Group the two samples together and sort the data in ascending order, but retain their sample origin. Step 2: Rank the combined data. Step 3: Separate the data back into the two samples. Sum the ranks for each sample. Step 4: Compute the test statistics U `U_1 = R_1 - (n_1(n_1+1))/2` and  `U_2 = R_2 - (n_2(n_2+1))/2` Step 5: Choose the small U to calculate the equivalent z-statistics. `z = (U - m_U) / sigma_U` where `m_U` and `sigma_U` are the