Anova and Nonparametric Test

After completing the "Applying ANOVA and Nonparametric tests" simulation, I realize there were a few things to take into consideration when analyzing a problem. This particular exercise wanted to know the differences and causes of the variation. In order to resolved the solution, an individual or whoever is conducting the analysis will need to know what type of test to used, decide if the null hypothesis should be rejected or not, and make recommendation based on the collected data. Due to two parameters, ANOVA and the Krusla-Wallis Test (Nonparametric) were used in the exercise.

The ANOVA was used for multiple studies and the population is assumed to be a normal distribution. Also, it can be used to test for significant differences between means. On the other hand, the nonparametric test makes no assumption about the type of distribution. I made a few mistakes because I had chosen the wrong test and failed to reject the null hypothesis. I misread the results and didn't notice the hypothesis test (H) exceeded the Chi-square critical value. I learned that it's very important to ensure the correct test is being utilized and the results are interpreted accurately. In my workplace, I can benefit from the ANOVA by comparing previous years and current budget within my unit. I'll be able to see the difference of the annual budget and actual expenses for each year. The variances will fluctuate but if there's a significant increase or decrease an evaluation will be required. This will help me to distribute funds based on the needs of each office and prevent my unit from under and over spending.

A business should produce quality, monitors production, measures and improves process when conducting a business. A wrong decision can impact a company as a whole such wasted funds and time. As a manager, these tests can help identify the process change and improve addressing quality issues.