Partial pooling through hierarchical and multilevel models resolves the reference class problem by combining information across different levels of grouping rather than selecting a single reference class. The approach automatically balances bias and variance by shrinking group estimates toward population means based on data, eliminating the need for philosophers to debate which comparison group is correct.