Signal correlation ( r s) is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>r</mml:mi><mml:mo>̂</mml:mo></mml:mover><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math>, between the average responses of two neurons to a set of stimuli suffers from confounding biases. The estimate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mover accent="true"><mml:mi>r</mml:mi><mml:mo>̂</mml:mo></mml:mover><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math> can be downwardly biased by trial-to-trial variability and also upwardly biased by trial...