DC-Den

You conducted your survey and collected your data. You plotted your charts and suspect a certain conclusion. Which test should you use to validate your hypothesis? Having learnt different statistical tools and showing their implementation in Excel LAMBDA, we want a reference to guide us. Qualitative and Quantitative Data The first is to distinguish the type of data. Data can be qualitative or quantitative . Qualitative data can be nominal or ordinal. Nominal data are named categorised data, for example gender:{male, female} and direction:{north, south, east, west}. Ordinal data are categories with implied order, for example size:{small, medium, large}, school grades:{A, B, C, D, E} and service rating:{very poor, poor, average, good, great}. The order however are not equally spaced, i.e. you cannot equate the difference between poor and average, with the difference between average and good. Quantitative data can be interval or ratio. Unlike qualitative data, quantitative data are equal

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