Q: How do you use Multiple Comparisons of Means (One-way ANOVA)?

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Null hypothesis of a one-way ANOVA is that the means are equal. Alternate hypothesis a one-way ANOVA is that at least one of the means are different.

ANOVA is an inferential statistic used to test if 3 or more population means are equal or to test the affects of interactions.

H_0:μ_(1∙)=μ_(2∙)=⋯=μ_(a∙) F=MSA/MSE F_(ν_1,ν_2 ) ν_1=a-1 , ν_2=(a-1)(b-1) H_0:μ_(∙1)=μ_(∙2)=⋯=μ_(∙b) F=MSB/MSE F_(ν_1,ν_2 ) ν_1=b-1 , ν_2=(a-1)(b-1)  

The F-test is designed to test if two population variances are equal. It compares the ratio of two variances. If the variances are equal, the ratio of the variances will be 1.The F-test provides the basis for ANOVA which can compare two or more groups.One-way (or one-factor) ANOVA: Tests the hypothesis that means from two or more samples are equal.Two-way (or two-factor) ANOVA: Simultaneously tests the hypothesis that the means of two variables from two or more groups are equal.

Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.

Related questions

ANOVA (Analysis of Variance) in psychology is a statistical technique used to analyze differences between group means in a study with multiple groups. It allows researchers to determine if there are significant differences between the group means and if those differences are likely due to the variables being tested rather than random chance. ANOVA is commonly used in experimental psychology to compare the effects of different experimental conditions or interventions on a dependent variable.

Null hypothesis of a one-way ANOVA is that the means are equal. Alternate hypothesis a one-way ANOVA is that at least one of the means are different.

The null hypothesis for a 1-way ANOVA is that the means of each subset of data are the same.

ANOVA test null hypothesis is the means among two or more data sets are equal.

ANOVA is an inferential statistic used to test if 3 or more population means are equal or to test the affects of interactions.

H_0:μ_(1∙)=μ_(2∙)=⋯=μ_(a∙) F=MSA/MSE F_(ν_1,ν_2 ) ν_1=a-1 , ν_2=(a-1)(b-1) H_0:μ_(∙1)=μ_(∙2)=⋯=μ_(∙b) F=MSB/MSE F_(ν_1,ν_2 ) ν_1=b-1 , ν_2=(a-1)(b-1)  

The F-test is designed to test if two population variances are equal. It compares the ratio of two variances. If the variances are equal, the ratio of the variances will be 1.The F-test provides the basis for ANOVA which can compare two or more groups.One-way (or one-factor) ANOVA: Tests the hypothesis that means from two or more samples are equal.Two-way (or two-factor) ANOVA: Simultaneously tests the hypothesis that the means of two variables from two or more groups are equal.

The ANOVA test means ANalysis of Varience and it is used to test for difference among group means. ---- That is the amount of variability between the means of the groups compared to the amount of variability among the individual scores of each group. Varience between groups versus varience within groups. Hope this helps...Natalie

a

No, don't use a single t-test to compare the means of 3 or more groups. Use ANOVA.

Analysis of Variance (ANOVA) compares 3 or more means. The t-test would only compare 2 means.

multiple means multiple, plus means plus.