I understand when performing a simple t-test, we typically control the type -1 error rate at $\alpha = .05$. This signifies that if the null hypothesis holds, the data will "incorrectly" reject the null hypothesis in 5% of all instances. Therefore, if we are performing 100 consecutive tests (and all of which the null hypothesis holds), we will reject the null in 5 of these tests. Thus, the need for multiple correction adjustment.
Currently, my statistics professor disagrees with my analysis. I will provide an example and then extrapolate further out:
My data frame contains two groupings of people (say, type A and type B). For each person we have 100 different measurements (things ranging from: height, weight, IQ, etc...). I want to run 100 different t-tests comparing the means of each of my covariates among Type A and type B people. Am I still required to use a multiple correction scheme here? I say no because these are different measurements between groups A and B. They say yes because I am performing multiple tests.
I happen to be the only statistician in my town. I am contracted to perform 100 different analyses (for 100 different researchers). Each researcher asks me to complete a single t-test. Do I need to perform a multiple test correction here? I would argue no though I fail to see the difference between this example and the previous.