Difference Between Anova and T Test
A t-test, sometimes called the Student t test is performed when you want to compare the means of two groups and see if they are different from each other. It is mainly used when randomization is given and it is given for only two and not more than 2 sets are in comparison. As part of the T-test, certain conditions are necessary to be implemented so that the results are correct. The key assumption is that the demographic data collected is normally distributed and that you’re comparing equal variances of the population. There are basic two types of T-test: independent measures t-test and paired t-test also known as the dependent t-test or paired t-test
When comparing two samples that are not matched pairs, or samples, the independent t-test is used. The second type, paired t-test, is used when the samples are presented in pairs. If you have more than two samples, analysis of variance should be used. It is possible to distinguish at least two ways from each other by carrying out several T tests, but it would be a great chance to make a mistake and then have a better chance to reach an incorrect result.
Anova test is popular for the analysis of variance. This technique is performed in the analysis of the effects of categorical factors. This test is used when there are more than two groups. They are basically t-test as well, but as mentioned above, it is used when there are more than two groups. The first assumption used in ANOVA test is that each sample that is to be used is chosen independently and randomly. Second, assume that people take tests as normal and have the same standard deviation.
There are four types of test variance. The first is one-way ANOVA. You should use such a variance if there is a definite factor. The second is a multivariate analysis of variance and is used when the factors are categorical and are many. The main effects and interaction between the factors is also considered. The third type of ANOVA is the analysis of variance components. This type of ANOVA is used when the factors are multiple and hierarchically organized. The main objective of this test is to determine the percentage of the variation of the processes that are introduced at each level. The fourth and last method is the general linear model.
2. T tests are used only when you have only two groups to compare. ANOVA test is a type of T test but is applicable only when the number of groups is more than 2.
3. Some things are necessary to be carried out before T tests are performed. For T-test, the demographic data collected is to be distributed normally, and you’re comparing equal variance of the population. While ANOVA tests for the samples to be used are chosen independently and randomly. We also assume that people whose samples are taken have the same standard deviation.