Here, I help you understand color blindness and describe a process by which you can make good color choices when designing for accessibility.
The opposite of color blindness is seeing all the colors and I can help you find 1,000 (or more) maximally distinct colors.
You can also delve into the mathematics behind the color blindness simulations and learn about copunctal points (the invisible color!) and lines of confusion.
In this section, I cover how to make good color choices when considering audiences with color blindness.
With the exception of the 8-color palette, all palettes have been created using a process (read below) that tries to maintain perceptual luminance uniformity in color-blind space.
This 8-color palette is adapted from Nature Method's Points of View: Color blindness by Bang Wong. Note that in that original source the RGB values listed in the table did not exactly correspond to the RGB swatches—probably an RGB vs CMYK conversion mixup.
This palette is suitable for categorical color encoding—the colors do not, as a whole, have a natural order and none is substantially more salient than another.
You can download these colors as plain text list of HEX and RGB values.
For more tips about designing with color blindness in mind, see Color Universal Design (CUD) — How to make figures and presentations that are friendly to people with color blindess.
To people with color blindness, some colors appear the same. This equivalence can be used to identify colors that are distinct to those with normal as well as to those with color blindness.
For a given RGB color we can simulate how it would appear to someone with color blindess and identify groups of RGB colors that appear indistinguishable in color blindness.
These equivalencies can be used to construct color palettes—lists of colors that are distinguishable to deuteranopes and those with normal vision.
Since deuteranopia is the most common, this is the condition that I use for color selection.
The exact luminance (perceived brightness) of the simulated color varies depending on the color blindness algorithm. Each row in the squares above should look identical using any color blindness simulation (e.g. Color Oracle, Photoshop, etc) but brightness of the rows may be slightly different than shown here.
This palette maps four colors onto each of the two color dimensions in deuteranopes and four onto greyscale. This palette is very useful for designing transit and subway maps.
Color names are playful selections from my list of 10,000 color names.
You can download these colors as plain text list of HEX and RGB values.
You can download these colors as plain text list of HEX and RGB values.
Even more color choices for color blindess, including colors that map onto greys. For these, I don't have RGB/HEX values handy.
You can download these colors as plain text list of HEX and RGB values.
You can create your own color palettes using the figure below.
For a given color blindness type (e.g. deuteranopia) and channel (e.g. blue), the rows represent reasonably uniform steps in LCH luminance of the simulated color and a rich (high chroma) simulation at that luminance.
It is not certain that everything is uncertain. —Blaise Pascal
We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.
This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.
Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.
My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.
Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.
Browse my gallery of cover designs.
Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.
Go ahead, see what you can't see.
Authentic and accurate images of Ishihara's test plates photographed (and lovingly color-corrected) from the 38-plate Ishihara's Tests for Colour Deficiency.
I also provide the position, size, and color of each circle on each test plate.
What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"
This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.
Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.
However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.
Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.