The magnitude of symptom inter-correlations in diagnosed individuals has contributed to the evidence that autism spectrum disorders (ASD) is a fractionable disorder. Such correlations may substantially under-estimate the population correlations among symptoms due to simultaneous selection on the areas of deficit required for diagnosis. Using statistical simulations of this selection mechanism, we provide estimates of the extent of this bias, given different levels of population correlation between symptoms. We then use real data to compare domain inter-correlations in the Autism Spectrum Quotient, in those with ASD versus a combined ASD and non-ASD sample. Results from both studies indicate that samples restricted to individuals with a diagnosis of ASD potentially substantially under-estimate the magnitude of association between features of ASD.