Understanding and explaining the causes of variation in food-chain length is a fundamental challenge for community ecology. The productive-space hypothesis, which suggests food-chain length is determined by the combination of local resource availability and ecosystem size, is central to this challenge. Two different approaches currently exist for testing the productive-space hypothesis: (1) the dual gradient approach that tests for significant relationships between food-chain length and separate gradients of ecosystem size (e.g., lake volume) and per-unit-size resource availability (e.g., g C m(-1) year(-2)), and (2) the single gradient approach that tests for a significant relationship between food-chain length and the productive space (product of ecosystem size and per-unit-size resource availability). Here I evaluate the efficacy of the two approaches for testing the productive-space hypothesis. Using simulated data sets, I estimate the Type 1 and Type 2 error rates for single and dual gradient models in recovering a known relationship between food-chain length and ecosystem size, resource availability, or the combination of ecosystem size and resource ability, as specified by the productive-space hypothesis. The single gradient model provided high power (low Type 2 error rates) but had a very high Type 1 error rate, often erroneously supporting the productive-space hypothesis. The dual gradient model had a very low Type 1 error rate but suffered from low power to detect an effect of per-unit-size resource availability because the range of variation in resource availability is limited. Finally, I performed a retrospective power analysis for the Post et al. (Nature 405:1047-1049, 2000) data set, which tested and rejected the productive-space hypothesis using the dual gradient approach. I found that Post et al. (Nature 405:1047-1049, 2000) had sufficient power to reject the productive-space hypothesis in north temperate lakes; however, the productive-space hypothesis must be tested in other ecosystems before its generality can be fully addressed.