Linear Regression Assumptions of Homoskedasticity When I studied linear regression analysis, one of the assumptions taught was that of homoskedatiscity. I understood that homoskedasticity was required for significance testing on the coefficients. Then in my econometrics class, my professor said that we actually don't need homogeneity assumption since it was too strong. Instead, in order to conduct hypothesis testing on the coefficients, we could use "t-robust test" or Wald test. 

So then why is homoskedasticity still widely assumed and taught in linear regression class? How do I reconcile these?
For the same reason you're taught the very simple supply and demand model of perfect competition in econ 101. It's not that it's wrong, it's a simplification of the real world and a good place from where to start. Once you've mastered the basics, you can learn more advanced topics. Heteroskedasticity isn't that advanced of a topic tho, most undergraduate students who take a second econometrics course get to learn about robust tests and all that.