### Summary

- Top of page
- Summary
- Introduction
- Characterizing intraspecific variation
- Case study
- Results
- Discussion
- Conclusions
- Acknowledgements
- References
- Supporting Information

- The importance of intraspecific trait variation is increasingly recognized, but the ways in which this variation is quantified and compared have not been rigorously assessed.
- We reviewed 5 years of ecological literature quantifying intraspecific variation (64 published studies) and identified commonly applied statistical methods. Analysis of variance techniques (
*n*= 43) was the most commonly applied method. Levene's tests (*n*= 14), linear techniques (both general and generalized models) (*n*= 12) and mixed effects modelling (*n*= 9) were also used. Qualitative comparisons of plant phenotype using descriptive statistics (*n*= 10) and coefficients of variation (*n*= 8) were also applied. Bayesian analysis was used in a single study. - We compared the efficacy and interpretation of analysis of variance, tests for homogeneity of variance, qualitative comparisons, mixed effects models and Bayesian hierarchical modelling in a case study measuring variation in specific leaf area (SLA) and rosette diameter among 10 populations (
*n*= 241 individuals) of*Hypochaeris radicata*. We also examined whether data base- and literature-based trait values provided good estimates for measured populations. - Intraspecific variation was substantial, and significant differences existed in both means and variation across populations for both measured traits. There was a 27-fold variation in SLA (1·7–46·1 mm
^{2}mg^{−1}) and a 34-fold variation in rosette diameter (1·7–59·1 cm). The choice of statistical technique influenced the interpretation of results. Permutational anova was reasonably successful in detecting differences among populations, particularly when combined with a permutational test of dispersion within populations. Only Bayesian estimates were able to simultaneously quantify and compare variation within and across populations and to estimate trait values and variation on a larger, regional scale. Literature-based trait values had poor fit for four of 10 populations and differed from the estimated regional trait distribution. *Synthesis*. Although both classical and Bayesian techniques yielded similar results, Bayesian techniques were more sensitive to differences in intraspecific variation, could simultaneously examine variation within and across populations, could estimate regional trait distributions and did not require that the assumption of homogeneity of variance be met. Bayesian techniques and hierarchical models in particular represent a powerful analytical tool for studies of intraspecific variation.