[daveguy]

Seaborn is a visualization library. No statistical tests should have been done with seaborn as an intermediate processing step. I guess they used some of the convenience functions as part of the data analysis. Seaborn is a final step tool, not a data analysis tool. That's an embarrassing lesson to learn post-publication.

[sitkack]

Take a look at the linked chart in my other comment. Visualization is absolutely a driver during research, it isn’t just an embarrassing revelation. Charts killed the Challenger crew.

[sillysaurusx]

> Charts killed the Challenger crew.

https://www.tiktok.com/t/ZT8oG7ym6/

This is one of my favorite TikToks of all time, and you’ll see why. It goes into detail about how charts killed the Challenger crew. But the storytelling is second to none.

[mannykannot]

I composed this comment concurrently with yours, so I'm moving it here as a response...

This is going off-topic, but Tufte's attempt to cast the problem as fundamentally one of poor data presentation is rather self-servingly tendentious, IMHO, in a way that unfairly attributes a degree of culpability to the engineers who tried to stop the launch.

The excellent video you link to, taken as a whole, supports this view, I believe.

[sillysaurusx]

That’s an interesting point (and quite on topic; communication is key in STEM, and this ties in with that).

Hypothetically, what would be the most fair argument in that situation? It’s quite remarkable that a line engineer was convinced the rocket was going to explode, even to the extent of hopping in his car with his daughter and frying to stop the launch after his company gave the go-ahead. Data presentation seems like one of the few things that could have convinced upper management that there was a serious problem.

One thing I don’t understand (possibly unrelated to your point): if there were very few launches in cold temperature in general, how could he have convinced himself that there was going to be a disaster due to the weather? If I were in his shoes, I might’ve talked myself out of it by saying "well, I suppose it’s true we don’t have much data about cold temperature launches; how certain am I that the cold weather problems till now weren’t a fluke or a non-issue?"

[mannykannot]

My position is not that a different or more thorough presentation of the argument would have made no difference (though, personally, I doubt that it would have), it is that Tufte's argument (and the aphorism it launched, "charts killed the Challenger crew") greatly exaggerates the significance this narrow issue and tacitly blames the people who were trying their best to save the day.

In Tufte's version, the meeting in question was the tipping point where it all went wrong, while the reality is that it was the last forlorn chance for NASA to to escape, by the skin of its collective teeth, from an overdue disaster that had been years in the making. As the Rogers Commission revealed, NASA had, in an environment of over-promising and political horse-trading, developed a culture in which deviance was normalized, and it was not ready to handle evidence contrary to the semi-official dogma of shuttle flights being routine and established events.

I'm not in a position to say how Boisjoly felt so sure the launch would end disastrously, but I can make a few guesses. I think it is quite possible that he gradually became aware, and then concerned, that the O-rings did not fare well in cold weather, as the data trickled in one launch at a time. I can imagine that when it became clear, a few days before the launch, that the temperature would be below freezing, his concerns sharpened into near-certainty that things would go wrong. One does not need a theory of what, precisely, was happening to the O-rings to suppose that if below-normal temperatures led to problems, then nothing good could come from an exceptionally low one. Perhaps he was too close to the data; I can also imagine that this seemed so clear to him that he never imagined his managers - who were also engineers - not also seeing it, instead clinging to older estimates of risk. I further imagine that he was completely blindsided by the somewhat rhetorical and sarcastic response, which went something like "are we supposed to wait until July?"

IIRC, Boisjoly anticipated that the joints would fail catastrophically immediately after the boosters were ignited, and for a minute afterwards he experienced profound relief...

Despite this all coming out in the Rodgers Commission report, NASA followed the same normalization of deviance path after it became apparent that ice was damaging the tiles, which is one reason why I doubt that better charts would have stopped the launch.

[sillysaurusx]

(Just wanted to say thank you for the thoughtful followup. I was hoping you would, since it sounded like it’d be interesting. And when you phrase it like you did, it does sound absurd to offload the blame onto whoever was presenting the charts. Enjoy the rest of your Sunday.)

[raziel2701]

He knew how O-rings behaved, they fail to make a seal at those low temperatures. You don't need to have multiple failed launches to reach the conclusion he reached.

[sillysaurusx]

You do, though. Because although there’s a chance of catastrophic failure, the typical case is that the launch goes fine, and then they notice some unexpected degradation of the O-ring after the shuttle comes back. That’s how they were measuring chance of O-ring difficulties; a shuttle had never exploded, but there had been observable signs that things could have gone badly.

In other words, in most mechanical systems, a certain amount of wear and tear is acceptable. It’s only at extremes (way too cold) that it becomes a disaster. Convincing yourself that you’re certain there will be a disaster this time is a level of scientific and engineering confidence that’s hard to fathom.

In that situation I would have done everything possible to alert management that there was a high chance of an issue, but would I have grabbed my daughter and drove to stop the launch because I was 100% certain it would blow? Probably not.

[ayhanfuat]

It can be a driver. But then you do a deep dive. You create frequency tables, you create crosstabs, you calculate summary statistics, you do inferential statistics. There is no excuse for not catching this pre-publication.

[sitkack]

That is not the point I was making.

[Aloisius]

The bulk of the problem was caused by erroneous metadata.

The bug in Seaborn simply meant that the histograms that could have alerted them that something was wrong with their analysis, didn't.