I have interviewed many statisticians over the years. One misconception many still have is that, “If we have more than 2 groups, then we can’t perform t-test and that is why we perform ANOVA”.
It is not that we can’t perform t-test with more than 2 groups. We certainly can.
The real reason why ANOVA came to be is because, doing a string of t-tests would lead to two things:
1) Multiple comparison issue (multiplicity).
2) Error inflation.
Remember that, each time one would conduct a t-test, there exists a possibility of making a type 1 error. As you conduct many such individual t-tests, you are simply compounding the type 1 error rate.
You could mitigate compounding these type 1 errors by running a single test – ANOVA.
One could then use Dunnett’s test as a post hoc test.
Resources:
Post hoc test ANOVA – https://statisticsbyjim.com/anova/post-hoc-tests-anova/
Type 1 error & Type 2 error – https://www.linkedin.com/posts/venkat-raman-analytics_statistics-machinelearning-datascience-activity-7025790308890140672-xKF8?utm_source=share&utm_medium=member_desktop
