Ishihara's Tests for Colour Deficiency
Authentic and accurate images of Ishihara's test plates
Just because it's black in the dark,
Oh, doesn't mean there's no colors.
— Laleh (Colors)
Authentic and color-accurate images of Ishihara's test plates for colour deficiency.
I provide high-resolution bitmaps and SVG files for each plate. I also provide the position, size, and color of each circle on each test plate.
Can this information be used to make fakes? Yes, but at least they'll be really good ones. Also, please don't.
If you're interested to learn more about colorblindess and the mathematics behind it, see my Designing for Color Blindness, Palettes for Color Blindness and Math of Color Blindness.
And turn those lines of confusion into understanding!
contents
All the resources on these pages are from an authentic 38 plate book. Plates, manual, and certificates were photographed from a 2007 issue of the 38 plate book. Cover images are from a 2020 issue.
Beware of all online Ishihara test resources that show images of test panels. Almost all of these images are either poorly color corrected or completely bogus.
Read my story of buying a fake book.
Fake test books and panels are everywhere. Real books come with this warning.
Depending on when your book was made, the notice may be slightly different. Below I show notices from 2007 and 2020 books.
Notice text is below. Paragraph breaks are mine.
The principle and basis for the test chart for color deficiency are the quality of the color and the color arrangement. Testing for color deficiency requires a high level of sensitivity and specificity.
Ishihara’s Test Chart has been used world wide because of its reliability and it is recognized for its high sensitivity and specificity in authorized ophthalmology textbooks.
The Isshinkai Foundation was founded by Professor Emeritus Shinobu Ishihara, The University of Tokyo, Japan. The Foundation owns the copyright of the Ishihara’s Test Chart for Color Deficiency. The Foundation has been maintaining this quality and certifies the Test’s reliability.
Imitation products have no scientific basis and might not be reliable, resulting in a high rate of false positives and false negatives. Misjudgement in color sensitivity could be very harmful to examinees.
The Foundation highly recommends that you do not use imitation or photocopied products but the original Ishihara’s Test Chart for Color Deficiency.
The Isshinkai Foundation
Notice text is below. Paragraph breaks are mine.
The principle and basis for the test chart for colour deficiency are the quality of the colour and the colour arrangement. Testing for colour deficiency requires a high level of sensitivity and specificity.
Ishihara's Test Chart has been used world wide because of its reliability and it is recognized for its high sensitivity and specificity in authorized ophthalmology textbooks.
Kanehara Trading Inc has been maintaining this equality and certifies the Test's reliability.
Imitation products have no scientific basis and might not be reliable, resulting in a high rate of false positives and false negatives. Misjudgment in colour sensitivity could be very harmful to examinees.
Kanehara Trading Inc highly recommends that you do not use imitation or photocopied products but the original Ishihara's Test Chart for Colour Deficiency.
KANEHARA TRADING INC.
Below are photos of the manual booklet. The full text is available here.
Below are images of all 38 Ishihara test plates (detailed list and colorblindness simulations).
These images are derived from photos that have been carefully color-corrected. If your monitor is calibrated, the colors will be accurate.
The numbers in the top left hand corner of each thumbnail are added for convenience and do not appear on the original plate.
de demonstration t transformation v vanishing h hidden d diagnostic
Propensity score weighting
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.
Nature Biotechnology cover
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.
Happy 2026 π Day—
Art for the 5%
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.
Ishihara's Tests for Colour Deficiency
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.
Symmetric alternatives to the ordinary least squares regression
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.