> the scatterplot chart isn't misleading

Even leaving out the data (which you rightly point out) you are forced to choose what to plot on x and y, which by convention will communicate IV and DV respectively whether you like it or not.

Hey, I am the author of this article. Really good point! I will try to think more deeply about whether some charts induce more of these effects than others.

In any case, what is interesting is that different charts have different ways of implying causality. This is what I try to do towards the end of the article.

You can check out some work of Howard wainer in this regard.

Graphic discovery, visual revelations etc.

https://en.m.wikipedia.org/wiki/Howard_Wainer

a famous example is that bar chart always better than pie chart (see the advice from page of pie chart on ggplot website).

It helps to internalize the concept that all statistics (visualizations, but also literally any statistic with an element of organization) is narrative. “The medium is the message” type of way.

Sometimes you are choosing the narrative consciously (I created this chart to tell a story), and sometimes you are choosing it unconsciously (I just want to scatter plot and see what it shows - but you chose the x and y to plot, and you chose the scatter plot vs some other framework), and sometimes it is chosen for you (chart defaults for example, or north is up on a map).

And it’s not just charts. Statistics on the whole exist to organize raw data. The very act of introducing organization means you have a scheme, framework, lens which with to do so. You have to accept that and become conscious of that.

You cannot do anything as simple as report an average without choosing which data to include and which type of average to use. Or a histogram without choosing the bin sizes, and again, the data to include.

This is all to say nothing of the way the data was produced in the first place. (Separate topic)

It's not a problem of interpretation or visualization or charts. People are talking about it as if it's deception or interpretation but the problem is deeper than this.

It's a fundamental problem of reality.

The nature of reality itself prevents us from determining causality from observation, this includes looking at a chart.

If you observe two variables. Whether those random variables correlate or not... there is NO way to determine if one variable is causative to another through observation alone. Any causation in a conclusion from observation alone is in actuality only assumed. Note the key phrase here is: "through observation alone."

In order to determine if one thing "causes" another thing, you have to insert yourself into the experiment. It needs to go beyond observation.

The experimenter needs to turn off the cause and turn on the cause in a random pattern and see whether that changes the correlation. Only through this can one determine causation. If you don't agree with this, think about it a bit.

Also note that this is how they approve and validate medicine... they have to prove that the medicine/procedure "causes" a better outcome and the only way to do this is to actually make giving and withholding the medicine as part of the trial.

People sometimes talk about this as “taking the outside view” or “reference class forecasting.” [1] It doesn’t work when there are important differences between the case being considered and other members of the reference class. Nationwide statistics are especially zoomed-out and there are going to be a lot of people in the same country who are quite different from you. Worldwide statistics are even worse.

It doesn’t mean the statistics are wrong, though. If there is a 70% chance of failure, there’s also a 30% chance of success. But it’s subjective: use a different reference class and you’ll get a different number.

The opposite problem is also common: assuming that “this time it’s different” without considering the reasons why others have failed.

The general issue is overconfidence and failure to consider alternative scenarios. The future isn’t known to us and it’s easy to fool yourself into thinking it is known.

[1] https://en.m.wikipedia.org/wiki/Reference_class_forecasting

It’s not even surprising without knowing about Pete: the newspaper isn’t going to publish the many that failed, so their own selection bias is the dominant effect. Even if we take the probability of the group to be the probability of individuals, eg, rolling dice (“1 in 6 people rolls a 4!”).

Lots of them opened, of them 70% failed, and one who didn’t happened to be named Pete.

No more interesting than “Pete rolled a 4!” even though 83% of people don’t.

I agree with your description, but the pizza place case is even simpler: Statistics don't guarantee future single sample properties. 70% fail in the first year. So, 30% don't. Why would it be surprising to see one that didn't? It would be surprising to see none that didn't fail. So, it's expected to see lots that don't fail, Pete's being one of them.