t-Test and ANOVA Testing- Classmate Response (1): Topic 5 DQ 1

t-Test and ANOVA Testing- Classmate Response (1): Topic 5 DQ 1


QUESTION-Compare the various types of ANOVA by discussing when each is most appropriate for use. Include specific examples to illustrate the appropriate use of each test and how interaction is assessed using ANOVA.


Classmate (Catherine) Response-

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The ANOVA, also known as analysis of variance, is a statistical method used to test if the results of a survey are significant or not. In other terms, the test is meant to determine whether a researcher should reject the null hypothesis or accept the alternate hypothesis (Mackenzie, 2018). The test is used to determine whether there is a difference between groups. For example, an ANOVA test can be used to determine which therapy for psychiatric patients between counseling or medication is more effective.

There are two types of ANOVA, ‘one-way’ ANOVA and ‘two-way ANOVA. One-way ANOVA has one independent variable with two layers. It is used to gain more information on the impact of the dependent variable on the independent variable. It can be used to determine the relationship between one or many dependent variables on the independent variable. Its null hypothesis (H0) is that there is no difference between the groups and equality between the means (Mackenzie, 2018). On the other hand, its alternate hypothesis (H1) is that there is a difference between the means of the groups. For example, it is used to determine the effectiveness of various types of medicines in treating malaria.

On the other hand, a two-way ANOVA is used to determine the impacts of more than one independent variable on a dependent (Mackenzie, 2018). For example, it is used in a statistical test examining the implications of age and gender and age on infant weight. While age and gender are independent variables, infant weight is a dependent variable.

Interaction effects arise when one variable depends on the value of another variable. An interaction effect occurs when the impact of one factor directly depends on the level of the other factor (Frost, J2021). For example, drug intoxication depends on the amount of alcohol or drug substances an individual consumes, such that the more alcohol consumption, the higher the intoxication levels.



Frost, J. (2021, June 10). Understanding interaction effects in statistics. Statistics ByJim. https://statisticsbyjim.com/regression/interaction-effects/

Mackenzie, R. J. (2018, July 20). One-way vs. two-way ANOVA: Differences, assumptions, and hypotheses. Informatics from Technology Networks. https://www.technologynetworks.com/informatics/articles/one-way-vs-two-way-anova-definition-differences-assumptions-and-hypotheses-306553


One-Way vs Two-Way ANOVA: Differences, Assumptions and Hypotheses

A key statistical test in research fields including biology, economics and psychology, Analysis of Variance (ANOVA) is very useful for analyzing datasets. It allows comparisons to be made between three or more groups of data. Here, we summarize the key differences between these two tests, including …


Understanding Interaction Effects in Statistics

Interaction effects occur when the effect of one variable depends on another variable. Learn how to interpret them and problems of excluding them.










Catherine, your post is quite informative. I agree with you that ANOVA, also known as analysis of variance, is a statistical process applied to test if the outcomes of a survey are essential or not. It is one of the most often used statistical approaches in medical research (Kim, 2017). This type of test help individuals comprehends how different groups respond, with a null hypothesis for a test that means different groups are equivalent. If there’s a statistically significant result, then it implies that the two populations are not equal. I support your point that ‘one-way’ ANOVA and ‘two-way ANOVA are some of the ANOVA types. One way ANOVA compares means of 2 or more independent groups defines whether statistical evidence that the related population means are meaningfully different.

On the other hand, Two-way ANOVA determines the effect of 2 insignificant predictor variables on constant outcome variables and analyzes the impact of independent variables on the anticipated outcome and their relationship to the outcome itself. There are some assumptions concerning these two types of ANOVA. For one, the results of one-way ANOVA may be well-thought-out as reliable provided that the following assumptions are met: that response variable residuals are generally distributed and that the variances of populations are equivalent (Sureiman & Mangera, 2020). In two-way ANOVA, the assumptions are that populations from which samples are attained should be generally distributed, observations for between and within groups should be independent, variances between populations should be equal, and besides, data are nominal and interval. Generally, care providers and marketers find it convenient to use the ANOVA test as it has the advantages of affording the overall equality test of group means,  control the overall rate of type I error and besides, it’s a parametric test, so it’s more influential if normality assumptions hold.


Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean Journal of anesthesiology70(1), 22. DOI: 10.4097/kjae.2017.70.1.22

Sureiman, O., & Mangera, C. M. (2020). Conceptual Framework on Conducting Two-Way Analysis of Variance. Journal of the Practice of Cardiovascular Sciences6(3), 207. DOI: 10.4103/jpcs.jpcs_75_20