#MannWhitneyUTest #WilcoxonTest #NonParametricStatistics #DataAnalysis #RProgramming #ggplot2 #StatisticalTesting #LearnR #DataScience #ResearchMethods #Microbiology #MarketResearch

Learn how to effectively compare two groups when data is not normally distributed using the Mann-Whitney U-Test (Wilcoxon Rank-Sum Test) in R programming. This video covers the basics of non-parametric testing, Shapiro-Wilk test for normality, data visualization with ggplot2, and interpreting the test results. Perfect for researchers in microbiology, psychology, market research, and more!


📌 Topics covered:

When to use the Mann-Whitney U-Test
Shapiro-Wilk test for normality
Data preparation with tidyr and dplyr
Visualization with box plots and density plots
Interpreting Wilcoxon test results in R
👉 Stay tuned till the end for tips on choosing between parametric and non-parametric tests!
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0:00 - Introduction to non-parametric testing
0:33 - Overview of Mann-Whitney U-Test
1:01 - When to prefer parametric tests
1:16 - Applications of Mann-Whitney U-Test
1:46 - Data description and setup in R
2:11 - Checking normality with Shapiro-Wilk test
3:10 - Transforming data to long format with tidyr
5:16 - Converting variables to factors with dplyr
6:14 - Visualizing data with box plots
7:44 - Density plots for group comparison
8:21 - Applying the Mann-Whitney U-Test in R
10:05 - Understanding W statistic and p-value
11:02 - Interpreting test results and alpha level
12:15 - Conclusion and key takeaways

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