UNIT VI JOURNAL
2
Unit VI Journal
ANOVA and t-tests are inferential parametric statistical procedures that look for the
differences between the variables or groups
(Liu & Wang, 2021)
. The difference between
them is that t-tests are used to compare two means, while ANOVA is used to compare more
than two means. The appropriate parametric statistical procedure between t-tests and ANOVA
to use when testing the hypothesis depends on the type of data being analyzed. The t-tests are
appropriate when we test the hypothesis by comparing the means of two groups, while
ANOVA tests are appropriate when testing the hypothesis of the research by comparing the
means of three or more groups
(Liu & Wang, 2021)
. The paper will discuss how ANOVA
tests could be used to compare means in the work environment and determine whether to
accept or reject null and alternative hypotheses using the ANOVA and t-tests.
ANOVA tests can be used to test hypotheses in business environments when comparing
more than two groups
(Liu & Wang, 2021)
. For example, marketers within the organization
want to determine which campaign will use to ensure effective and increased sales of the
product. The ANOVA tests can be used to compare the means of multiple groups that can be
assigned to take different advertising campaigns like TV commercials, social media,
newspapers, or roadshows so as to determine which campaign will have the best outcome in
increasing the sales of the product. This shows that ANOVA tests enhance compare means of
multiple groups that enable the marketers to draw valid inferences about the similarities and
differences between the groups and pick the best campaign to ensure the best increase in sales
of the product.
ANOVA tests are used when determining whether to reject or accept a hypothesis. As
such, to determine whether to reject or accept a hypothesis, the two-parametric statistical
procedure requires one to perform appropriate statistical tests and calculate the p-value
(Liu
& Wang, 2021)
. When analyzing the variances using the one-way ANOVA or single-factor
