Question

T-tests and ANOVAs are inferential statistics. What is the difference between them?

Answer 1

difference between T-test and Anova are as follows:

T-test	ANOVA
T-test tests for differences between means of two independent groups. ANOVA tests fro differences between means for 2 or more groups.	ANOVA test for variation between the means and within each mean. That is, ANOVA uses a different calculation. Its value is in looking at variation between and within the means. It not only compare the means (like the t-test) but examines the variation in calculating the means. Excessive variation in calculating a mean reduces the chance of determining a significant difference.
T-test is a hypothesis test that is used to compare the mean of two population	ANOVA is a statistical technique that is used to compare the mean of more than two populations.
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.	When we use ANOVA, it is assumed that the sample is drawn from the normally distributed population and the population variance is equal
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	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.