One Way Anova Hypothesis Example. At least one population mean is different from the rest. A tukey post hoc test revealed significant pairwise differences between fertilizer types 3 and 2 with an average difference of 0 42 bushels acre p 0 05 and between fertilizer types 3 and 1 with an average difference of 0 59 bushels acre p 0 01.
A one way anova is used to compare two means from two independent unrelated groups using the f distribution. Reporting the results of a one way anova we found a statistically significant difference in average crop yield according to fertilizer type f 2 9 073 p 0 001. Mean1 mean2 mean3 meanx under the null hypothesis ssx and sserror come from the same source of variation.
A one way anova is used to compare two means from two independent unrelated groups using the f distribution.
Reporting the results of a one way anova we found a statistically significant difference in average crop yield according to fertilizer type f 2 9 073 p 0 001. Anova allows one to determine whether the differences between the samples are simply due to random error sampling errors or whether there are systematic treatment effects that causes the mean in one group to differ from the mean in another. In one way anova the interest lies in testing the null hypothesis that the category means are equal in the population. A one way anova is used to compare two means from two independent unrelated groups using the f distribution.
