This video covers the assumptions of linear regression that must be met for your test to be valid. These assumptions include: outliers, normally distributed errors, homoskedasticity, and linearity. If these assumptions are violated, it is likely your test is biased or invalid.
Kelsey Emnett, Data Scientist 