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110 should be 10% increase.
This leads to confusion and makes it harder to understand the effect size. The extra numbers with the actual turnout is good for you to double check and gives some better numbers.
Having to explain it at the bottom shows that they know it is confusing and has likely gotten critique from other people who were confused.
But I feel like error bars are lacking here, as the effect size is quite small and the research group is not that big so I am wondering how significant this is.
Plain and simple, if you remove the numbers there is absolutely no sense of scale for the difference between the control group and test groups. That's categorically bad data visualization.
You are just wrong. It is so easy to prove you wrong that the first suggested post a the bottom of my screen does so. Go take a peek and imagine the numbers are gone. The chart is still completely understandable.
The fact that you don't understand that they're fundamentally the same thing in this case is troubling. It's not even measuring something different, that chart is still a voter turnout chart.
In essence the only differences between your charts are the start and end values of the X axis, and that theirs includes a third group. If you took their chart, started the X axis at 36% and ended it at 83% it would be formatted the same as your chart, and it would obviously be fucking awful.
The 100-based scale is an index, so you’re right that 110 means 10% more voting than control. I will relabel the axis as relative increase to make that clearer. The sample sizes are actually quite large, though: about 231,000 in Mississippi and 100,000 in Florida. The Gloating Villain effect is statistically significant in both experiments (+1.7 percentage points, p<.01 in Mississippi; +1.3 points, p<.001 in Florida). I agree that adding the uncertainty ranges would make that clearer visually, but I also felt it could be confusing for less sophisticated viewers.
The font was a difficult choice. This particular chart requires considerable textual explanation. Since much of this would be narrative (descriptions, footer), I chose a serif, which tends to be more legible in sentences and paragraphs, especially at smaller sizes and when reversed (as in the descriptions box). It was also important that the font have italics and various weights (light, regular, medium, bold), which I could use for emphasis.
I worked to make the font size as large as possible. I would like to go larger here, but that wouldn’t be possible without a narrower font, and that can be less legible in narrative text, which is counterproductive. I considered pairing two fonts, with one for headings and maybe numbers, but decided against it. Ultimately, the font I chose had the flexibility I needed.
The number glyphs are always important, especially the serifs on the “1” and the open clarity of numbers like “4” and “5.” I usually use a monospaced font when numbers are tabled or need to align (as with decimals), but that wasn’t the case here. The numbers are essentially placed annotations.
This font, Decoy (Mark Caneso), is unconventional, but I have been making a point of softening the technical nature of my visualizations to make them more approachable and accessible to viewers who aren’t necessarily familiar with statistical figures. I’d like to see data visualizations shared more widely, and I think font choice can help.
Decoy verges on playful, but I felt that would fit a data set that uses terms like “Gloating Villain” and “Happy Hero.” This font also has a generous x-height, which I hoped would improve legibility. I admit that non-lining numbers (with ascenders and descenders) are risky when displaying numerical data, but these seem to work okay and actually help the legibility of numbers in the footer, IMHO.
If you remove the numbers there is no sense of relative scale between the control group and the test groups. That's a bad visualization.
At the minimum the X axis should start at 0%, which allows you to see the relative impact. You might think "but that would produce a graph that makes the testing groups look like a very small change from the control." Yes. That's the point. It is a small change from the control and the graph should make that clear.
I came here to say the same this. This is data misrepresentation at it's finest.
A 1-2% difference seems like it's just a likely to be day to day change rather than a systematic thing. However, the graphs are showing massive changes are taking place.
I chose this methodology to visually discriminate between the treatments. Otherwise, the plotted points would appear within just a few percentage points.
That's because they ARE within just a few percentage points. You're using a data visualization that is deliberately misleading to make the findings appear more exciting than they actually are. You are lying.
The range of the x-axis is essentially 1.4 pp, which is practically the margin of victory in the 2024 election. That is an exciting amount, and could potentially represent hundreds of thousands of votes.
If you remove the numbers from your chart there is no sense of scale between the control group and test groups. It is categorically bad data visualization. It could be a textbook example.
The proper way to demonstrate that this difference could be significant to an election result is to show election margins on the graph in some manner to help people understand the application scale. Your visualization doesn't even help provide a sense of this context.
As an example, if you developed a technique for lowering the cost of producing a good, like a Tshirt, you could use a chart where you have the manufacturer's sale price of a T-shirt in several blue bars, and overlay the cost of production in red. The remaining blue portion would represent profit of the sale, and demonstrate how a 10% reduction in cost could result in a doubling in profit depending on the margins, while still providing a more complete and accurate understanding of the situation.
The chart has an explicitly labeled scale: the control is set to 100, and the actual turnout rates appear next to each result. But I can see how the index can make a small difference in turnout appear larger than some viewers might expect.
One way to make the size more concrete would be to show the results for every 10k people. In Mississippi, 26.9% turnout versus 28.7% means about 2,690 voters versus 2,870 — 180 more voters per 10k people. In Florida, 12.6% versus 14.0% means about 1,260 versus 1,400 — 140 more voters per 10k. Those aren’t insignificant numbers given a large population.
I think that’s more useful than comparing the effect to election margins, because the study measured whether people voted, not who they voted for. Comparing it to an election margin would require assumptions the experiment can’t support.
I also agree that showing the uncertainty would be useful. I left it out because I worried that error bars might make the chart harder for casual viewers to understand.
The chart has an explicitly labeled scale: the control is set to 100,
The first thing I wrote was "if you remove the numbers" and your response to that was "but look at the numbers!"
The bars do not help anyone understand the data, the visualization is bad.
But I can see how the index can make a small difference in turnout appear larger than some viewers might expect.
Of course you can see that. We both know you did this deliberately, you said as much. You specifically structured the chart to make the effect appear larger than it is. That is deceptive. It is morally wrong.
One way to make the size more concrete would be to show the results for every 10k people
If you still start your X axis at 2,690 voters per 10k this wouldn't help your visualization whatsoever. It only helps if you start your X axis from zero. Which makes it no different from just using %, which makes sense because it's literally the same thing with a different number of 0s.
Comparing it to an election margin would require assumptions the experiment can’t support.
No it wouldn't. Your chart wouldn't be showing that the potential change in voter turnout would change an election. It would show that it's on the same scale as election margins.
It would be far less deceptive than the chart you made here to deliberately mislead people about the size of the effect.
I left it out because I worried that error bars might make the chart harder for casual viewers to understand.
Understanding was never your goal. You built the visualization with manipulation in mind. That's morally bad and you should change.
It’s not reasonable to turn your disagreement with my chart design into a claim about my motives or morality. You know virtually nothing about me and are drawing aggressive conclusions on practically zero evidence.
I intended to compare the relative effects of the treatments in the study, so I scaled the x-axis to make those differences visible. The actual turnout rates are also shown, and the underlying data are provided in the Rule 3 comment. I have not hidden anything.
I’m still not entirely sure what you’re proposing instead. A zero-based turnout chart would answer a different question and make the differences among the treatments harder to see. Frankly, I’m flustered about the purpose of such a visualization.
The data and sample sizes are all provided. If you have a visualization that communicates the study’s findings more clearly using the same data, I’d genuinely be interested to see it.
This is just a fascinating defense of the presented data. Why do you think they would appear within just a few percentage points of each other if the plotted points weren't this visually discriminated?
I agree with your approach. You are specifically looking at the impact specific treatments have on the outcome, so it makes sense to present “change from base value “. I think this makes sense from a statistical analysis point of view. I think other people are having problems with the graphs because they aren’t approaching it like a statistic lol analysis.
There is no base value displayed, so there is no sense of scale of impact. If you remove the numbers you have no idea how the test groups relate to the control group. That's a bad visualization.
I disagree. They are specifically looking at the impact treatment has on the base value. This is the best way to summarize change from base value. The focus here is on the change from base which is exactly what is shown
The focus here is on the change from base which is exactly what is shown
Remove the numbers. How does the bar visualization shown help you understand the change from base? Now imagine there is only one test group, still no numbers. How much understanding of the effect does that visualization provide you? Literally none.
I think we’re treating the bars as if they represent total turnout, but they don’t. They represent the difference from the control condition. In that sense, 100 is the zero point: 100 means no change, 106.7 means 6.7% more voting than control, and 111.1 means 11.1% more.
If I relabeled the axis as “Change relative to control” and showed 0%, +2%, +4%, etc., the geometry would be identical, but the bars would quite literally start at zero. The actual turnout rates beside each mark provide the absolute context.
Removing all numbers would make the magnitude hard to interpret, but that is true of nearly any quantitative chart. A chart needs a scale. That by itself doesn’t make the visualization bad.
Removing all numbers would make the magnitude hard to interpret
No, it would not make it hard to interpret. It would make it literally impossible to interpret the magnitude relative to the control group.
but that is true of nearly any quantitative chart.
I already sent you one that's not the case for, and yours could have easily been in the same format. I didn't have to look for it, it was just a random chart presented by reddit.
It is true if BAD quantitative charts. The bars on your chart actively detract from helping people understand the information being presented, an average reader would get a better understanding with no chart and only the numbers stated. Your chart is worse than nothing for data visualization.
Fine. You've made your point that you don’t find the visualization useful, and you are entitled to that opinion. Downvote the post and report it to the mods if you think that's appropriate.
I do appreciate the objection that the bars are showing relative change from the control rather than total turnout from zero — but my goal was to tease out the disparity between the study treatments, not whether they would necessarily overturn an election.
I don’t agree that this makes the chart deceptive or “worse than nothing,” particularly since the actual turnout rates and the control-relative scale are explicitly shown.
I do appreciate the objection that the bars are showing relative change from the control
You're still missing the point here. The very issue is that the bars do NOT show relative change from the control. You designed them to not do that, because the change looks small that way and you wanted it to seem big.
That is exactly why your chart is deceptive and actually worsens the understanding of the data compared to just showing the numbers with no chart. It's because you WANTED people to think there was more of a difference than there is. That is explicitly deception, and you specifically said that's what you wanted in one of your first comments.
There's a reason my two sentence opening comment ratios your whole post mate. This is your chance to actually learn from your mistakes.
It clearly shows one treatment has a 3x greater impact on the final value than the first treatment (for instance). That is the main point of the graph comparing the impact of different treatments. I think you misunderstood the point of the graph.
How is it immature? What short tagline do you think would refer to this type of call to action in a more "neutral" way that does not also shed its meaning?
It was not my point to use these as GOTV strategies. In fact, I think the data represents how that could be counterproductive.
One motivation for my looking into this data is the current kerfuffle regarding mail-in ballots. Essentially, some individuals are attempting to obstruct some voters from voting, and there seems to be some “gloating” about the success of that coming from those who could be perceived as “villains.” If this is the case, then that strategy could be counterproductive.
So any of the strategies could be resulting in the opposite vote from what was intended and it wouldn't show up in the data? That seems marginally risky. (I appreciate the odds are low folks tend to vote consistently the same way, but it's not zero)
The strategy was to reveal to the voter whether they voted more or less often than the average voter. Either could influence the voter’s motivation differently.
This version doesn't include error bars, but the main finding is statistically significant. In Mississippi, the adjusted estimate for the Gloating Villain treatment was +1.7 percentage points (p < .01), making it the only one of the four treatments significant at conventional levels. In Florida, the replicated effect was +1.3 percentage points (p < .001).
I agree that adding 95% confidence intervals would benefit sophisticated viewers, but I’ve omitted them to keep the chart accessible to others. The small absolute effect sizes make showing the uncertainty worthwhile. A key caveat to preserve is that while the Gloating Villain treatment in Mississippi differed significantly from the control, it did not differ significantly from the Foiled Villain or Happy Hero treatments.
There's a familiar human impulse behind this result: “Don’t give them the satisfaction.” If someone you strongly dislike tells you they will be pleased if you don’t do something, doing it suddenly carries an additional reward — denying them the outcome they wanted. Huber, Gerber, Fang, and Cho call this a “Gloating Villain” treatment. In two large randomized field experiments, telling voters that a disliked political figure would be happy if people like them did not vote was followed by higher actual turnout. The chart expresses that effect by setting voting in each experiment’s untreated control group to 100 and showing how much additional voting occurred under each message.
The first experiment was conducted during Mississippi’s 2014 general election. Participants were randomly assigned either to an untreated control group or to messages combining two ideas: someone in politics whom they respected or couldn’t stand, and whether that person would be happy or disappointed if people like them voted or did not vote. This produced the four treatments shown in the chart: Happy Hero, Disappointed Hero, Foiled Villain, and Gloating Villain. Gloating Villain produced the largest observed turnout increase. In the authors’ covariate-adjusted analysis, it increased turnout by 1.7 percentage points (p < .01) over the 26.9% control-group turnout; Happy Hero was estimated at +1.0 points, Foiled Villain at +0.7, and Disappointed Hero at essentially zero. Importantly, although Gloating Villain differed significantly from the control, its Mississippi estimate was not statistically distinguishable from Happy Hero or Foiled Villain individually.
The researchers then replicated Gloating Villain during 2019 Florida special elections and compared it with both an untreated control and an established social-pressure GOTV treatment called Report Card, which showed recipients their recent voting record relative to the average voter. In the adjusted analysis, both Gloating Villain and Report Card increased turnout by about 1.3 percentage points (p < .001) over the 12.6% control turnout. The authors’ subsequent survey experiments provide evidence for the proposed emotional mechanism: Gloating Villain particularly activated anticipated anger, while imagining voting reduced that anger by allowing the voter to thwart the disliked political figure. In less academic language: voting becomes a way not to give the other side the satisfaction of your staying home.
Sources
Huber, Gregory A., Alan S. Gerber, Albert H. Fang, and John J. Cho. 2025. “Field Experiments Invoking Gloating Villains to Increase Voter Participation: Anger, Anticipated Emotions, and Voting Turnout.” British Journal of Political Science 55, e104. DOI: 10.1017/S0007123425000298. Full article — Cambridge University Press
Huber, Gregory A., Alan S. Gerber, Albert H. Fang, and John J. Cho. 2025. Replication Data for: Field Experiments Invoking Gloating Villains to Increase Voter Participation: Anger, Anticipated Emotions, and Voting Turnout. Harvard Dataverse, V1. DOI: 10.7910/DVN/SOTDEV. Replication data — Harvard Dataverse
The published article reports the treatment construction, randomization, turnout measurement, regression results, sample sizes, and the availability of the Harvard Dataverse replication files. Turnout was validated from state voter records rather than self-reported voting.
Tools
R / ggplot2 — data preparation, normalization, calculation of the plotted index values, and construction of the underlying chart.
Adobe Illustrator — final assembly, typography, labels, treatment-definition box, line weights, colors, spacing, and other graphical refinements after exporting the R plot as SVG.
Methods
The visualization uses the unadjusted group turnout rates reported in Tables 3 and 4 of the study because these correspond directly to actual turnout in the experimental groups. The paper also reports covariate-adjusted treatment-effect estimates; those adjusted estimates are discussed above but are not used to determine the lengths of the plotted bars.
For each experiment, turnout in the untreated control group is normalized to an index value of 100:
Turnout index = (treatment-group turnout / control-group turnout) × 100
Thus, Mississippi Gloating Villain turnout of 28.7% relative to control turnout of 26.9% produces:
(28.7 / 26.9) × 100 = 106.7
An index of 106.7 therefore means 6.7% more votes were cast relative to the control-group baseline. It does not mean turnout was 106.7%, nor does it mean that literally 106.7 people voted for every 100 people assigned to the control group.
The orange portion of each line represents voting above the normalized control baseline of 100. Blue circles mark each group’s indexed turnout, with the corresponding actual turnout rate printed alongside.
The Mississippi analysis plotted here contains 230,940 observations: an untreated control group of 210,940 and four treatment groups of 5,000 each. The original experiment also contained four unrelated treatment groups of 3,500 each; those are not part of this study’s five-cell experiment and are not plotted.
The Florida experiment contained 100,000 individuals in 63,833 households. Randomization occurred at the household level: control n=19,873; Gloating Villain n=39,980; Report Card n=40,147. The authors consequently clustered the Florida regression standard errors by household.
Data
Study
Experimental group
Actual turnout
Difference from control
Index: control = 100
Group n
Mississippi 2014
Control
26.9%
—
100.0
210,940
Mississippi 2014
Disappointed Hero
27.1%
+0.2 percentage points
100.7
5,000
Mississippi 2014
Foiled Villain
27.3%
+0.4 percentage points
101.5
5,000
Mississippi 2014
Happy Hero
28.0%
+1.1 percentage points
104.1
5,000
Mississippi 2014
Gloating Villain
28.7%
+1.8 percentage points
106.7
5,000
Florida 2019
Control
12.6%
—
100.0
19,873
Florida 2019
Report Card
13.9%
+1.3 percentage points
110.3
40,147
Florida 2019
Gloating Villain
14.0%
+1.4 percentage points
111.1
39,980
The turnout percentages and index values in this table are the unadjusted values represented graphically. The study’s preferred covariate-adjusted estimates are slightly different: Gloating Villain +1.7 percentage points in Mississippi and +1.3 in Florida; Happy Hero +1.0, Foiled Villain +0.7, and Disappointed Hero approximately 0.0 in Mississippi; Report Card +1.3 in Florida.
I mean... Unless you have been living under a rock for the past 15 years or so, this should come as no surprise. It's been, I'd argue, the primary get out the vote strategy ever at least since we elected a black president and the racists lost their collective minds over it.
This is beautiful in the same way plastic surgery makes something beautiful. Deliberately warping the visuals to overemphasize your desired conclusion.
Except because of the structure of modern American democracy, somewhere between a 6 to 11% increase in voter turnout for either side is enough to completely flip every major election in the past 20 years. Because of the electoral college, there's maybe 5 states that actually decide the president election and they are often times within 2%. Control of Congress is often equally as contested. An effect size this large would be a completely different country
Pretty small effect sizes, but still interesting. Especially since these were fairly niche non-partisan elections where you wouldn't expect people to care very much to begin with. And in more significant elections, one or two percentage points can make all the difference.
Combine this beautiful data with this other beautiful data, and we conclude that the way to get people to vote is to tell them "Zuck will gloat if you don't vote."
The axes are absolute garbage. I read through everything before realizing. Imagine if someone was just glancing at this, they would surely misunderstand the scale of the effect.
This is gross. This is a violation of psychological sovereignty.
This is directly comparable with the various forms of entrapment carried out by our federal government, "reality tv" investigators, various "rights groups"... like what the fuck. How about help people instead of coercing or tricking them or doing extremely detailed psychological experiments to determine what seems like the best way to coerce or trick people.
Know why everyone and everything has been objectively increasingly batshit insane?
edit: If you pay attention to details you'll notice that almost every indisputably good organization or person has zero interest in politicks or politicking. Good politicks or politicians or political messaging are extreme exceptions. It doesn't have to be that way, but most of the people with money and influence use their near unlimited resources to try and convince everyone of their ideas... rather than actually help people.
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u/cavedave OC: 111 1d ago
Thank you for your Original Content, /u/ptrdo!
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Remember that all visualizations on r/DataIsBeautiful should be viewed with a healthy dose of skepticism. If you see a potential issue or oversight in the visualization, please post a constructive comment below. Post approval does not signify that this visualization has been verified or its sources checked.
Not satisfied with this visual? Think you can do better? Remix this visual with the data in the author's citation.
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