In agreement with the other comments, this is indeed a very dense diagram, specifically the right-side. Focusing on that some more, my chief concern is that this novel triangle representation is very easy to misread.

Let’s take the dot in the middle which has the arrow with “10M”. What would you say the car percentage for that dot is? The axis along the bottom of the triangle is labeled 0 to 100%, and the dot is just to the right of the 50% demarcation. So maybe 52% or 55% seems reasonable, yeah?

But the axis is deceiving: notice how the demarcation are all slanted at the bottom. The dot is actually representing about 42%, since although the axis is marked horizontally, the line which is 50% slopes north-east rather than straight up. You can see the 50% number itself is actually rotated 60 degrees counter-clockwise.

The public transit axis on the left of the triangle has its demarcations tilted clockwise by 60 degrees as well. Only the active transport axis matches the conventional Y axis.

For that UI/UX reason alone, I wouldn’t endorse this as a “great” depiction of statistical data. If a diagram can – intentionally or not – be used to mislead a casual reader, it’s not one we should put up on a pedestal.

I also had a gripe about the successive colors not being consistent for each mode of transport, but that’s minor and easily corrected. The tilted axes may require some reworking though.

Obligatory link to Statistics Done Wrong: The Woefully Complete Guide, a book on how statistics can and has been abused in subtle and insidious ways, sometimes recklessly. Specifically, the chapters on the consequences of underpowered statistics and comparing statistical significance between studies.

I’m no expert on statistics, but I know enough that repeated experiments should not yield wildly different results unless: 1) the phenomenon under observation is extremely subtle so results are getting lost in noise, 2) the experiments were performed incorrectly, or 3) the results aren’t wildly divergent after all.

I’m reluctant to upvote this, since it’s leaving out a lot of rather important caveats about the dataset. This depiction is presented as “the number of aviation incidents between the two giants since 2014 in the U.S. and international waters”. Here, “international waters” means the regions of the North Pacific Ocean, north Atlantic Ocean, and Gulf of Mexico, whose airspace services are delegated by ICAO to the United States, administered by the FAA. It’s not US airspace, but it’s administered as if it was, meaning accident reports get filed with FAA and NTSB, the source of this data.

The other caveat is that the total size of the Boeing fleet flying through FAA-administered airspace versus the total Airbus fleet is closer to 2-to-1, with nearly twice as many Boeing aircraft as Airbus aircraft, using 2018 estimates. This is including all the aircraft which US airliners currently operate, not just the newest ones they’ve bought in recent years.

Finally, in the reporting parlance, an aircraft “incident” means a non-serious injury event that happened. If major injuries or death occurred, that would be an “aircraft accident”. So an incident could include anything like:

• Returning to the airport because of an unruly passenger
• Another aircraft getting too close but not requiring evasive manoeuvres (aka minimum separation violation)
• Overspeeding of the aircraft, such as exceeding 250 knots while still below 10,000 ft
• An engine failure
• A door plug falling off, causing minor injuries to three people but no deaths
• A passenger getting their arm stuck in the toilet while reaching for their dropped phone

What reasons could Boeing aircraft have more incidents? Sure, they might be shoddily assembled. But it could also be a matter of fleet distribution: if Boeing makes more wide-body aircraft than Airbus, and thus carry more passengers, then passenger-related incidents would be higher represented for Boeing aircraft. Suffice it to say, this single graphic isn’t giving enough depth to a complicated situation.