 T-test and Analysis of Variance abbreviated as ANOVA, are two parametric statistical techniques used to test the hypothesis. As these are based on the common assumption like the population from which sample is drawn should be normally distributed, homogeneity of variance, random sampling of data, independence of observations, measurement of the dependent variable on the ratio or interval level, people often misinterpret these two. Here, is an article presented for you to understand the significant difference between t-test and ANOVA, have a look. Content: T-test Vs ANOVA
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Definition of T-testThe t-test is described as the statistical test that examines whether the population means of two samples greatly differ from one another, using t-distribution which is used when the standard deviation is not known, and the sample size is small. It is a tool to analyse whether the two samples are drawn from the same population. The test is based on t-statistic, which assumes that variable is normally distributed (symmetric bell-shaped distribution) and mean is known and population variance is calculated from the sample. In t-test null hypothesis takes the form of H0: µ(x) = µ(y) against alternative hypothesis H1: µ(x) ≠ µ(y), wherein µ(x) and µ(y) represents the population means. The degree of freedom of t-test is n1 + n2 – 2 Definition of ANOVAAnalysis of Variance (ANOVA) is a statistical method, commonly used in all those situations where a comparison is to be made between more than two population means like the yield of the crop from multiple seed varieties. It is a vital tool of analysis for the researcher that enables him to conduct test simultaneously. When we use ANOVA, it is assumed that the sample is drawn from the normally distributed population and the population variance is equal. In ANOVA, the total amount of variation in a dataset is split into two types, i.e. the amount allocated to chance and amount assigned to particular causes. Its basic principle is to test the variances among population means by assessing the amount of variation within group items, proportionate to the amount of variation between groups. Within the sample, the variance is because of the random unexplained disturbance whereas different treatment may cause between sample variance. With the use of this technique, we test, null hypothesis (H0) wherein all population means are the same, or alternative hypothesis (H1) wherein at least one population mean is different.
The significant differences between T-test and ANOVA are discussed in detail in the following points:
ConclusionAfter reviewing the above points, it can be said that t-test is a special type of ANOVA that can be used when we have only two populations to compare their means. Although the chances of errors might increase if t-test is used when we have to compare more than two means of the populations concurrently, that is why ANOVA is used
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Why use ANOVA instead of multiple tests when testing means differences between multiple groups?
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