There are number of common practices which are used to mislead on purpose. The point is to show main tricks they use and to educate users to critically think about data thats presented to them.

This recent r/dataisbeautiful post is an excellent example of misleading data visualization:

https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/

See this comment for explanation:

https://old.reddit.com/r/dataisbeautiful/comments/z8tl1f/oc_ever_wondered_which_are_the_top_20_biggest/iyd5goo/

I’m not sure I’d agree that 1 is not ok in most instances. It’s okay if you’re comparing values against a reference, particularly if you’re trying to show outliers.

Take, for example, 100 meter dash times. There’s a huge difference between 10.0 and 9.9 seconds (a body length). …and if you’re trying to compare Usain Bolt’s record against the other fastest times, you would need to truncate the axis to see that his fastest stands out against the next 9 fastest runners which are clustered together.

There just one example but there’s plenty of others.

What I read in Dutch news papers is that research has shown that mass movements likely result in revolution. However, with social media, it is easier to create a large following. Sadly, this following is also more fleeing, especially since these uprisings lack a leader or other organisational attributes.

Because that analogy just doesn't map to the situation here. There aren't certain plotting/graphing practices that are more likely to be associated with misleading data then they are with accurate data (except maybe not putting labels on your axis). You are making the assumption that if you see plots that do this, they are more likely to be misleading than accurate, but I don't think the data support that claim. I do everything on this list all the time in my job as an engineer, and I am doing it because it's the most accurate way to answer the questions that my data were collected to answer.

I mean there's nothing to the left of the bars specifying the unit and scale... Is it percentage points? 0-100 or 0-10? We have no clue. It ties into the first point noting how important the scale is to putting the data in context.

EDIT: I also have no idea how I made the text large but I have been watching a 6 day old baby since 4am and cannot be held responsible 😂

There are instances where starting the Y-axis not at zero is okay. I'm bad at examples, but zero is used as a baseline. Which means that it would be better to start the Y-axis at another value, it being similar to the baseline.

An example might be the concentration of something, like CO2 molecules in the atmosphere. It is impossible and irrelevant to get to 0. Besides, it's not about the absolute values but the relative values.

But in general, yeah, it is misleading.

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Certainly helps explain why there are so many +50% counties right next to -20% counties.

If I travel to another country, I would like to know about common tourist scams, so if for example someone wants to sell me a bracelet on the street, I will be extra careful with the purchase. I’ll approach carefully, ask questions, evaluate situation, etc. Why cant we apply same principle here?

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Bruh it’s not a voucher to go see some people milking cows somewhere. You have to transport 40 women cows and buckets to your house in order to present them as a gift. That means a bus for the maids, trucks for the cows, and a serious amount of planning.

for the accurate chart in point 2, is the Y-axis not evenly spaced out? the gap between 60 and 80 seems much smaller than 20 and 40, even though they're the same increment of 20%

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You see it a lot on scientific papers, it was more common before but you do still have a few papers that come out with a badly scaled Y axis to insist on the difference between group A and group B.

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Almost all of them scams or involved in defrauding people.

Is this ChatGPT?

I have to disagree with #2. I'm a fan of this blog post by datawrapper, which features this graphic (and has more arguments against dual axis charts besides being misleading). you can manipulate the axes to show literally any correlation that you want, which is a pretty fatal flaw imo for any data visualization

Journalists should know this, it’s not complicated. Assume intent to mislead. Or that they’re too dumb to be doing their job.

Exactly, there are many examples on how they do it. People should be aware of this.

Slave wages

Thank you for saying this. I was thinking have these companies gone up THAT much in just a few years?

It’s a Spanish name, so usually people of Hispanic descent.

It means “daisy.” The drink is also named after the flower.

Based on the constant Aramco ads I'm being bombarded with, I think they're trying to change that.