Data visualization uses three distinct types of color encoding, each with different design requirements. Categorical encoding uses color to distinguish unordered groups (product categories, countries, species) — the colors should be maximally distinguishable from each other while avoiding any implication of ordering. Sequential encoding uses color lightness or saturation to represent a continuous ordered variable (temperature, population density, probability) — the color progression should be perceptually uniform so that equal data differences produce equal perceived color differences. Diverging encoding represents variables with a meaningful midpoint (positive vs. negative deviation, comparison to average) — two hue sequences meet at a neutral midpoint color, showing direction as well as magnitude.
The most pervasive data visualization color mistake is applying categorical colors to ordered data, or sequential colors to categorical data. A bar chart showing five product categories should use five distinguishable, perceptually equal colors — not a five-step gradient from light to dark, which implies that the last category is 'more' than the first. A choropleth map showing population density should use a sequential color scale — not five arbitrary categorical colors, which obscures the underlying ordering. The choice of encoding type is a data structure decision, not an aesthetic one: it should be determined by whether the variable being represented is nominal (unordered), ordinal (ordered), or continuous (numeric).
Colorblind accessibility in data visualization requires designing for the 8% of men and 0.5% of women with some form of color vision deficiency. Deuteranopia and protanopia (red-green colorblindness) are the most common forms. The most common visualization mistake for these users: using red for 'bad' and green for 'good' without any other distinguishing encoding. The solution is not to eliminate color, but to add redundant encoding: shape, pattern, position, or text labels that convey the same information the color is encoding. A well-designed accessible visualization uses color as one of multiple encoding channels, not the sole encoding channel for critical information.
