Understanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population-level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV-environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal, Peromyscus maniculatus across gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population-level ITV for both in silico and Peromyscus maniculatus populations. Furthermore, the influence of sample size on detecting ITV-environment relationships depends on how sample sizes and population-level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV–environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV-environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients.