Diffusion MRI (dMRI) can probe the tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution is combined with high diffusion encoding strengths. Low SNR leads to poor precision as well as poor accuracy of the diffusion-weighted signal; the latter is caused by the rectified noise floor and can be observed as a positive bias in magnitude signal. Super-resolution techniques may facilitate a beneficial tradeoff between bias and resolution by allowing acquisition at low spatial resolution and high SNR, whereafter high spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using phantom experiments and numerical simulations, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. By contrast, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find a gain in precision from super-resolution reconstruction is substantial only when some spatial resolution is sacrificed. Finally, we deployed super-resolution reconstruction in a healthy brain for the challenging combination of spherical b-tensor encoding at ultra-high b-values and high spatial resolution-a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. This demonstration showcased that super-resolution reconstruction enables a vastly superior image contrast compared to conventional imaging, facilitating investigations that would otherwise have prohibitively low SNR, resolution or require non-conventional MRI hardware.