Statistics Anova Testing

Introduction In order to test the adverse reactions of the drug called Xynamine (a new drug designed to lower pulse rates) must be tested for their effectiveness and the drug is tested only for males. Here, the treatment groups are divided into 3 categories they were placebo group (no treatment), 10-mg treatment group and 20-mg treatment group. Thus, the analysis test result is carried out using the technique of one-way analysis of variance ANOVA (Analysis of Variance - single factor). Assumptions of ANOVA The assumptions of ANOVA are: Observations were randomly and independently chosen from the populations, population distributions are normal for each group; and population variances are equal for all groups. The assumptions of ANOVA are identical to the t-test and the calculated statistic is called an F-value which has a probability value associated with it. As with the t-test, if our probability value is less than 0.05 we reject our null hypothesis (in this case that there is no difference among the treatment groups). This p-value only tells if there are significant differences among our groups. It does not tell us where these differences are. In other words, in an experiment with three treatment groups and a significant p value, we know that there are some differences among these groups but we do not know specifically which groups are different. As a result, ANOVA is usually performed in conjunction with a post hoc multiple comparisons test (e.g. Bonferoni’s test or Tukey’s test) that will tell you precisely where the differences lie. A set of data can only “reject” a null hypothesis or “fail to reject it”. In our case, if comparison of three groups (10-mg treatment, 20-mg treatment, and no treatment) reveals no statistically significant difference between the three, it does not mean that there is no difference in reality. It only means that there is not enough evidence to reject the null hypothesis (in other words,