The lecture focuses on the sixth step of interpreting the results obtained from the ANOVA and Chi-square test. Before the actual interpretation, the material presents a background of both tests. It is quick to relate the ANOVA test to previous tests that include the t-test and the F-test. As discussed in the earlier tests, the ANOVA test is also a hypothesis testing and follows all the first four steps of hypothesis testing. Its fifth step also shares similarities with the f-test because it is found in the data analysis tab. One has to select a specific test to be performed. The discussed case uses the ANOVA- single factor option and leaves the remaining two-factor with replication and without replication for the next lesson. Other similarities include the output table that provides useful information other than the p-value being sought and the assumptions one has to make of equal variance.
The chi-square test, on the other hand, also has some similarities to the previous tests, although it presents a different approach in comparing the groups. First, it follows the first four steps of hypothesis-testing. In the fifth step, data is set in a table marking the first difference. It is also different because, unlike the other tests that focus on the data parameters such as mean and variance, the chi-square is interested in the distribution. However, it is achieved the same way since it seeks to find whether the p-value obtained from the Fx statistical list obeys the rejection or acceptance rule.
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Though the material is mostly clear, it is, however, not clear on the extent the assumption of equity invariance holds. Just as the discussed example shows the difference in variances, the lecture notes does not directly guide on how unequal the variance should be for the use of ANOVA to be inaccurate. Therefore, there is need for clarification.
In conclusion, the material presented is very relevant to the degree area of study because it guides one in making data-backed decisions (Kowalski, Kowalski, & Lasley, 2008) . In most instances, data is resented in various groups. Unlike the t-test and f-test that limits the number of data sets being compared, the ANOVA and chi-square present a more realistic way of analyzing a wide range of data sets presented in a real-life situation. Thus it emphasizes its importance in research in the course of the degree. Furthermore, the focus on the distribution rather than the data parameter makes chi-square more relevant in the analysis of the most continuous data in real life.
Reference
Kowalski, T., Kowalski, T., & Lasley, T. (2008). Handbook of data-based decision making in education . New York, NY: Routledge.
