What is the difference between T-TEST and ANOVA?

Statistical hypothesis tests to determine if the null hypothesis is supported is a T-test. It uses a student's t-distribution for this purpose. Before the t-test can be used, the test statistic must follow a normal distribution, and there should be a known value for the scaling term. However, in the event that the value of the scaling term is unknown, the use of an estimate that corresponds with the data is used.

It is believed that William Sealy Gosset, a chemist from Ireland, was the first person to introduce a t-test in 1908. ANOVA or Analysis Of Variance is a statistical method and is a collection of statistical models. It is used mostly to compare the means or averages of different groups in order to know if they are the same or not. Although the t-test can also be used for this purpose, ANOVA is usually a better method compared to a t-test because the latter is vulnerable to type I error.

T-Test and Anova are related to samples. T-Test is defined as a hypothesis test. It compares the means of any two given samples. ANOVA stands for Analysis of Variance. It is also related to the means of any two given samples, but it is a statistical technique.

They are often misinterpreted because they are both based on hypothesis and a common assumption that people have about a population or statistic. Another difference between the two is the type of test statistic. T-test has a test statistic of (x Ì„-µ)/(s/√n), while Anova has a test statistic of the sample variants between and within

The t-test happens to be a statistical hypothesis test whereby a student's t-distribution is followed by the test statistics if it supports the null hypothesis. You apply this test when there is a normal distribution of the test statistics, and you know the scaling term value in the test statistics. This t-test statistics was introduced by William Sealy Gosset, a chemist for the Guinness brewery in Ireland, in the year 1908.

Generally, t-test statistics follow the form T=Z/s, where, s, and Z are the data functions. On the other hand, ANOVA (the variance analysis) is a collection of statistical models. ANOVA has actually been in use by statisticians and researchers for quite a time but was proposed to be formalized in an article written by Sir Ronald Fisher in the year 1918.

Unlike the t-test that is used in comparing two means, ANOVA is generally employed in determining the relationship between three or more means. The t-test might tend to commit error when being engaged in larger means, reason why ANOVA is employed in more means.