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In a Continual Learning setting, models are trained on data with occasional distribution shifts, resulting in forgetting the information learned before each shift. Experience Replay (ER) addresses this challenge by retaining part of the old training samples and replaying them alongside current data, improving the model's understanding of the overall distribution in training batches. The crucial factor in ER performance is the diversity of samples within batches. The impact of sample diversity across a sequence of batches is investigated, introducing a new metric and an associated approach to assess and leverage this diversity. This exploration opens up significant potential for future work, as various strategies can be devised to ensure inter-batch diversity. Achieving optimal results may involve striking a balance between this novel metric and other inherent properties of a batch or sequence.