If your goal is to compare means of two or more batches of data, and you're willing to make the usual parametric assumptions (errors are normally and independently distributed with equal variance), then:
1. When there are two batches to compare, the statistical results of the t-test will lead to exactly the same p-values, decisions, and conclusions as the F-test. In fact, if you square the obtained t-statistic, it will equal the F-ratio that would be observed for the data set.
2. When there are more than two batches to compare, only the F-test is capable of simultaneously comparing all for equality (e.g., Ho: mu1 = mu2 = ... = muk, with k batches, k > 2).
the t-test is used for small samples as a test of significance for single mean, and the difference between two means( related and independent samples). While f-test (ANOVA) is used to compare means between multiple samples( more than two).
1.The F-test can be used for large sampled population.
2.The T-test is used to compare the means of two different sets. It decides whether the mean of one group is significantly different from the other group. See the link: https://www.aatbio.com/resources/faq-frequently-asked-questions/What-s-the-difference-between-an-F-Test-and-T-Test#:~:text=The%20F%2Dtest%20can%20be%20applied%20on%20the%20large%20sampled%20population.&text=The%20T%2Dtest%20is%20used,different%20from%20the%20other%20group.