Analysis of Variance is also known as ANOVA

Analysis of Variance is also known as ANOVA

Analysis of Variance is also known as ANOVA; this is a statistical technique used to describe the variance of two or more averages of a sample data also known as mean through testing the means to identify their differences (Weerahandi & Krishnamoorthy, 2017). The two means are from the experiment sample and the control sample; the ANOVA test concerning two means only will give similar results as the t-test for the samples used; the t-test is a statistical hypothesis tests that follows a Student’s t-distribution for the test statistic under the null hypothesis. The ANOVA test allows one to decrease the type 1 error probability during the testing of multiple two-sample t-tests; this tests the trueness of the Null Hypothesis.
ANOVA considers the ultimate sample in its collections means and how the means are spread in each sample and parallels the means to the expected spread means if all the collections were from the same ultimate sample (Unroe ; Stump ; Effler ; Tu ; Callahan, 2018). This makes ANOVA an observation and experimental exercise for testing hypotheses: The ANOVA tests are further categorized into; One-way ANOVA, Two-way ANOVA and Three-way ANOVA. The One-way ANOVA considers only a single factor in the study experiment; the Two-way ANOVA considers two factors during the tests; the Three-way ANOVA considers three factors in the study.
This test is used in medical professional when testing the effectiveness of a new medication; the ANOVA results will be used to compare the control experiment outcomes and the experiment outcomes; this makes it easier for physicians to easily tell the treatment’s effectiveness from the results of the analysis (Mertler & Reinhart, 2016). This can also be applied in researches and even practical home settings of the effectiveness of a certain behavior or new ideas compared to the old models.


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