Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. ANOVA was developed by statistician and evolutionary biologist Ronald Fisher. In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether the population meansof several groups are equal, and therefore generalizes the t-test to more than two groups. ANOVA is useful for comparing (testing) three or more group means for statistical significance. It is conceptually similar to multiple two-sample t-tests, but is more conservative, resulting in fewer type I errors,[1] and is therefore suited to a wide range of practical problems.
Use probability levels. Maximum allowable probability of committing a type I error is the level of significance. Suppose, you are conducting the test at 5% level of significance (alpha = 0.05). It means if the p value of your test result is less than 0.05, you are to reject your null hypothesis. In case of 1% level of significance the p value of the test should come under 0.01 to reject the null hypothesis. It means there is significant difference among groups. So, comparison on the basis of p value (probability value) is the standard procedure followed worldwide.
Thanks.
What do you mean by 'better to use'? When I have large samples, I know the p values will be small. In general I look at the ratio between F-ratio and degrees of freedom. One could expect the ratio to be 1 by 'chance'. P values can be made to go to below 0.05 just by having big sample sizes. This could be a problem with BIG DATA. It can be significant, but hardly relevant and low percentage of explained variance.
dear Peter Moorer tnx for answer
I want to show the results of analysis of variance tests in the table. Sometimes the mean squares, sometimes probability levels, and sometimes non-significant, are used. Which one is more reasonable?
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