Colorimetry can be computed for a given observer, illuminant and a surface, using the standard CIE formulae, resulting in a discrete point in three dimensional space, the CIE XYZ tristimulus value. While other representations are also possible (such as CIE LAB, LCh etc.), they all ultimately share one characteristic, that color is represented as a point in 3D space. However, there is known uncertainty and variation in every one of the stimuli, and quantities from which colorimetry is computed and perceptual attributes predicted. Likewise, there is uncertainty in perceptual evaluation – not only as a function of stimulus properties (size, context) but also fundamental variability between observers. The representation of color as a discrete point in 3D however obscures this underlying variability. In this paper an alternative is put forth, which claims that representing colors as points is both reductive and inaccurate when it comes to reflecting real-world performance and also – perhaps counterintuitively – introduces unnecessary complexity when designing optimization problems. Not only is it more correct to consider colorimetry in terms of ranges or probability distributions, it also leads to more realistic ways to talk about, e.g., whether or not two samples match, whether or not a colorimetry is in-gamut or not, and ultimately also helps in formulating color and machine learning models that open up new possibilities in using the latest AI techniques.