The study explores how conventional standard errors can be too large in certain situations, making it difficult to detect heteroscedasticity. This can lead to inaccurate results when testing for causal effects. By understanding when standard errors are too large, researchers can improve the accuracy of their tests and potentially gain more power in detecting causal effects.