Rather than writing about a specific person, I wrote a blog post on Why Ada Lovelace Day is Important. It includes a review of a thorough study on gender bias among science faculty published a few months ago. It’s really distressing to me that even in 2012 there exists this much male privilege in science academia.
Please fix your chart. The origin of the y axis is at 25000 rather than at zero, which makes a 15% difference appear as a 200% difference visually. When comparing two values, proportion is as vital as magnitude.
Why should the origin of the y-axis be 0 rather than 15000, or wherever the average minimum wage falls, or what the average 5th percentile lab manager wages are? When comparing two values, deciding which proportion to report can determine which values are actually being compared.
Upvoted. Several times I’ve seen recommendations to start graphs’ y axes at zero by default, but it’s a tip that’s starting to grate on me for several reasons.
Usually, when I look at a graph, the y values’ variation is at least as relevant as the values themselves. I want that variation to be clear & obvious; if someone’s going to represent it on a graph, I want it spread across the available space. Cramming it into a small range near the top is a waste.
Visually compressing variation can be just as misleading as visually expanding it. Which is more misleading is case-dependent.
Sometimes I want to read numbers off a graph as accurately as I can. If the plotter stretches the y axis because they think I’m too dumb to read labels, that makes my task harder.
If the y axis is on a log scale, you can’t make it go to zero without some distracting gimmick like making the axis discontinuous.
For me points 1 & 2 apply here. (Although, as it happens, I don’t like that figure 2. It’s too close to a dynamiteplot for comfort, and it’s a space-hungry way to show me two averages & two standard errors. You could communicate the same information with a small table, or even a line of prose. And Kindly’s right about the caption. But starting the y axis at $25k is the least of that chart’s problems.)
These are excellent points. Unfortunately, I’m a bit hampered by the fact that I stole the chart in question from the original study (pdf), and they used only “dynamite plots” in their paper. After reading your links on the topic, I can definitely see why this is bad. I’m appending a short note to this effect as an edit to my original article.
Thank you for bringing this stuff to my attention.
Normally I’m not so stuck on having 0 be the bottom of a graph, but this is a case where there’s no reason for anything else. You’re comparing only two things, so you aren’t zooming in to help the reader pick out fine gradations of detail.
Rather than writing about a specific person, I wrote a blog post on Why Ada Lovelace Day is Important. It includes a review of a thorough study on gender bias among science faculty published a few months ago. It’s really distressing to me that even in 2012 there exists this much male privilege in science academia.
Please fix your chart. The origin of the y axis is at 25000 rather than at zero, which makes a 15% difference appear as a 200% difference visually. When comparing two values, proportion is as vital as magnitude.
Why should the origin of the y-axis be 0 rather than 15000, or wherever the average minimum wage falls, or what the average 5th percentile lab manager wages are? When comparing two values, deciding which proportion to report can determine which values are actually being compared.
At the very least the y-axis should match the caption which says “The scale ranges from $15000 to $50000”.
Upvoted. Several times I’ve seen recommendations to start graphs’ y axes at zero by default, but it’s a tip that’s starting to grate on me for several reasons.
Usually, when I look at a graph, the y values’ variation is at least as relevant as the values themselves. I want that variation to be clear & obvious; if someone’s going to represent it on a graph, I want it spread across the available space. Cramming it into a small range near the top is a waste.
Visually compressing variation can be just as misleading as visually expanding it. Which is more misleading is case-dependent.
Sometimes I want to read numbers off a graph as accurately as I can. If the plotter stretches the y axis because they think I’m too dumb to read labels, that makes my task harder.
If the y axis is on a log scale, you can’t make it go to zero without some distracting gimmick like making the axis discontinuous.
People can’t decide whether this rule applies to bar charts specifically or graphs in general.
For me points 1 & 2 apply here. (Although, as it happens, I don’t like that figure 2. It’s too close to a dynamite plot for comfort, and it’s a space-hungry way to show me two averages & two standard errors. You could communicate the same information with a small table, or even a line of prose. And Kindly’s right about the caption. But starting the y axis at $25k is the least of that chart’s problems.)
These are excellent points. Unfortunately, I’m a bit hampered by the fact that I stole the chart in question from the original study (pdf), and they used only “dynamite plots” in their paper. After reading your links on the topic, I can definitely see why this is bad. I’m appending a short note to this effect as an edit to my original article.
Thank you for bringing this stuff to my attention.
Normally I’m not so stuck on having 0 be the bottom of a graph, but this is a case where there’s no reason for anything else. You’re comparing only two things, so you aren’t zooming in to help the reader pick out fine gradations of detail.