Chen's test is a statistical test used to compare the means of two independent groups when the data is not normally distributed. It is also known as the Mann-Whitney-Wilcoxon (MWW) test.
How Chen's Test Works
Chen's test is a non-parametric test, meaning it does not rely on assumptions about the distribution of the data. It compares the ranks of the data points in the two groups rather than the raw values. The test calculates a statistic, known as the U-statistic, which measures the difference in the ranks between the two groups.
When to Use Chen's Test
Chen's test is appropriate when:
- The data is not normally distributed.
- The groups are independent.
- The data is ordinal or continuous.
Examples of Using Chen's Test
- Comparing the effectiveness of two different medications: You could use Chen's test to compare the effectiveness of two different medications for treating a specific illness, even if the data on patient outcomes is not normally distributed.
- Comparing the performance of two different teaching methods: You could use Chen's test to compare the performance of two different teaching methods, even if the data on student scores is not normally distributed.
Interpretation of Results
The results of Chen's test are presented as a p-value. A p-value less than 0.05 indicates that there is a statistically significant difference between the means of the two groups. This means that the difference in the means is unlikely to have occurred by chance.
Conclusion
Chen's test is a useful tool for comparing the means of two groups when the data is not normally distributed. It is a non-parametric test that is relatively easy to perform and interpret.
