We analyze the formal grounding behind Negative Correlation (NC) Learning, an ensemble learning technique developed in the evolutionary computation literature. We show that by removing an assumption made in the original work, NC can be seen to be exploiting the well-known Ambiguity decomposition of the ensemble error, grounding it in a statistics framework around the biasvariance decomposition. We use this grounding to find bounds for the parameters, and provide insights into the behaviour of the optimal parameter values. These observations allow us understand how NC relates to other algorithms, identifying a group of papers spread over the last decade that have all exploited the Ambiguity decomposition for machine learning problems. When taking into account our new understanding of the algorithm, significant reductions in error rates were observed in empirical tests.