# Difference Between Anova and T-test

**Anova vs T-test**

A T-test, sometimes called the Studentâ€™s T-test, is conducted when you want to compare the means of two groups and see whether they are different from each other. It is mainly used when a random assignment is given and there are only two, not more than two, sets to compare. In conducting the T-test, some conditions are needed to be met so that the results will render accurate results. The primary assumptions are that the population data to be gathered are normally distributed and that you are comparing equal variances of the population. The T-test has two main types: Independent Measures T-test and Matched Pair T-test also known as the Dependent T-test or Paired T-test.

When you are comparing two samples that are not matched pairs, or the samples are independent, the Independent T-test is used. The second type, Matched-pair T-test, however, is used when the given samples appear in pairs. For example, you are to measure between before and after comparisons. If you have more than two samples, then the Anova Test should be used. It is possible to differentiate more than two means with each other by conducting multiple T-tests, but there would be a big possibility of making a mistake and, therefore, having a bigger chance of arriving with an inaccurate result.

The Anova test is the popular term for the Analysis of Variance. It is a technique performed in analyzing categorical factors effects. This test is used whenever there are more than two groups. They are basically like T-tests too, but, as mentioned above, they are to be used when you have more than two groups. Anova tests use variances to know whether the means are equal or not. Before performing an Anova test, you should fulfill the basic assumptions first. The first one assumption is that each sample that is to be used is selected independently and is random. Second, assume that the population you are taking the samples from is normal and have equal standard deviations.

There are four types of Analysis of Variance tests. The first one is the One-Way Anova. You are to use this type of Anova only if there is just one categorical factor. Second is the Multifactor Anova which is used when the categorical factors are more than one. Interactions and main effects between the factors are estimated. The third kind of Anova is the Variance Components Analysis. This type of Anova is used when the factors are multiple and hierarchically arranged. The main goal of this test is to know the percentage of the process variability that you are introducing in each level. The fourth and last method is the General Linear Models. If your factors are both nested and crossed, some of the factors are random and some are fixed. When both factors present are quantitative and categorical, this test is used.

Summary:

1.The Anova test has four types, namely: One-Way Anova, Multifactor Anova, Variance Components Analysis, and General Linear Models. T-tests have only have two types: Independent Measures T-test and Matched Pair T-test that is also known as the Dependent T-test or Paired T-test.

2.T-tests are only conducted when you only have two groups to compare. Anova tests, on the other hand, are basically just like T-tests but it are designed for groups that are more than two.

3.Some conditions before performing the two tests are needed to be accomplished. For the T-test, population data to be gathered should be normally distributed, and you are comparing equal variances of the population. While for Anova tests, samples that are to be used are selected independently and randomly. You should also assume that the population you are taking the samples from is normal and have equal standard deviations.

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