When Graphs Are a Matter of Life and Death (newyorker.com) 122
Pie charts and scatter plots seem like ordinary tools, but they revolutionized the way we solve problems. From a report: John Carter has only an hour to decide. The most important auto race of the season is looming; it will be broadcast live on national television and could bring major prize money. If his team wins, it will get a sponsorship deal and a chance to start making some real profits for a change. There's just one problem. In seven of the past twenty-four races, the engine in the Carter Racing car has blown out. An engine failure live on TV will jeopardize sponsorships -- and the driver's life. But withdrawing has consequences, too. The wasted entry fee means finishing the season in debt, and the team won't be happy about the missed opportunity for glory. As Burns's First Law of Racing says, "Nobody ever won a race sitting in the pits."
One of the engine mechanics has a hunch about what's causing the blowouts. He thinks that the engine's head gasket might be breaking in cooler weather. To help Carter decide what to do, a graph is devised that shows the conditions during each of the blowouts: the outdoor temperature at the time of the race plotted against the number of breaks in the head gasket. The dots are scattered into a sort of crooked smile across a range of temperatures from about fifty-five degrees to seventy-five degrees. The upcoming race is forecast to be especially cold, just forty degrees, well below anything the cars have experienced before. So: race or withdraw?
This case study, based on real data, and devised by a pair of clever business professors, has been shown to students around the world for more than three decades. Most groups presented with the Carter Racing story look at the scattered dots on the graph and decide that the relationship between temperature and engine failure is inconclusive. Almost everyone chooses to race. Almost no one looks at that chart and asks to see the seventeen missing data points -- the data from those races which did not end in engine failure.
One of the engine mechanics has a hunch about what's causing the blowouts. He thinks that the engine's head gasket might be breaking in cooler weather. To help Carter decide what to do, a graph is devised that shows the conditions during each of the blowouts: the outdoor temperature at the time of the race plotted against the number of breaks in the head gasket. The dots are scattered into a sort of crooked smile across a range of temperatures from about fifty-five degrees to seventy-five degrees. The upcoming race is forecast to be especially cold, just forty degrees, well below anything the cars have experienced before. So: race or withdraw?
This case study, based on real data, and devised by a pair of clever business professors, has been shown to students around the world for more than three decades. Most groups presented with the Carter Racing story look at the scattered dots on the graph and decide that the relationship between temperature and engine failure is inconclusive. Almost everyone chooses to race. Almost no one looks at that chart and asks to see the seventeen missing data points -- the data from those races which did not end in engine failure.
Graphs (Score:5, Interesting)
One of my two jobs revolves around generating graphs and data tables, as well as analyzing the outputs.
In business, at least, very few people understand them, and even fewer are making decisions based on those graphs and data tables.
Re:Graphs (Score:5, Interesting)
Often I find the person in charge is often the least able to interpret the data to make informed decisions.
For most businesses the Owner is also the founder of the organization. They often got their company by grit, force of will and making gut decisions. This is usually perfectly fine for an organization until it reaches a particular size (usually to a point where the boss cannot manage every person in the organization (around 100 employees) After that point, you are going to need to to be able to understand data in aggregate. Then know enough to ask questions and find problems that are creating the numbers. (To de-average the data)
For too many bosses when a company gets past being a small company, to a mid-sized company, when they get graphs and charts, they will often be happy because the line for say profit is still going up. or they will freak out when an other metric goes down. They are not accustom to seeing so much data, and knowing what is the normal variance, and how to judge shape of data.
So if I were to see a company profits going on a log scale over time, it is still going up, I would be concerned because, or at least start asking questions, because I would hope it would be more linear. But I am not the boss, I am a data guy. I may recommend my interpretation of the data to them, however still the Boss who is often the founder, who got there with gumption and instincts may not really listen to it, and go on their own way. Until the company fails.
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Re: Graphs (Score:5, Interesting)
Often I find the person in charge is often the least able to interpret the data to make informed decisions.
Yeah. Not so much the founders of a business. But after a few generations, management tends to be populated by people with high verbal/social skills and less math/logic/visual. The smart ones recognize their shortcomings and keep a few math geeks close by on their staff. Not so smart tend to get corralled onto the golf course by the court jesters and steered by politics.
Re:Graphs (Score:4, Funny)
Often I find the person in charge is often the least able to interpret the data to make informed decisions
Something tells me that's not exactly a controversial stance hereabouts.
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You want to see pain, go to reddit's wallstreetbets community and look at the lines people draw on graphs there. It'd be hilarious if it wasn't for all the people throwing money at stuff based on them.
Re: Graphs (Score:4, Insightful)
"Look, I can graph this data, but it would be helpful to know what you want it to show so we don't waste time trying to present objective truth"
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The article is by Hannah Fry (Score:1, Offtopic)
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You're so stuck on looks you forgot to mention she's also an all-around badass as decided by Hanna Fry, who is a Mathematician, science presenter so you know it's true.
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She has a number of videos on Numberphile. Entertaining and educational.
https://www.youtube.com/playli... [youtube.com]
Re:The article is by Hannah Fry (Score:4, Interesting)
Re:The article is by Hannah Fry (Score:4, Insightful)
That really is inappropriate. You do not need to sexualize everything. Women are sexualized enough in daily life.
We're animals - sexualizing everything is pretty much what we do. We also admire and value intelligence, strength, independence, creativity, etc. I admire them in women, and my wife embodies all of them. I treat her with respect, and I also sexualize her. She returns those favours. And we both sexualize other members of the opposite sex - often simultaneously, such as when we're watching a movie with attractive characters.
Your naive, politically correct wokeness will cause you and others more damage than you now imagine. Men seeing women (and vice versa) solely and primarily as sexual objects is destructive, unfair, and dangerous. The big "but" here is that men ignoring women's sexual attractiveness and their own response to it, (and vice versa) is equally destructive, unfair, and dangerous. Furthermore, it results in hiddenness and repression, which tend to explode unpredictably at some point or other.
A lot of wokeness overreaches; it goes beyond prescribing compassion, respect, awareness, and decency and tries to dictate how people should feel. That's fundamentally immature, it doesn't work, it never will, and you need to get over it.
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Wish I had mod points. Someone mod this up "Insightful".
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We also live inside cultures and in a civilization, the main purposes of which are to modify, adapt, and sometimes override our animal nature for the greater good. Behaviors that go beyond base animal behavior are not only possible and desirable, but commonplace.
Re: The article is by Hannah Fry (Score:2)
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Yawn. If people posting online are able to slow to your brand of progress, then it is a weak wheumy form of progress indeed.
Re:The article is by Hannah Fry (Score:4, Insightful)
Sure it does, when you define anything you disagree with as being "woke".
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Sexuality and reproduction are all evolution cares about. Business and intellectual achievement are just feathering one's nest to attract the best mate possible.
That's the giant cosmic joke on all the increasing equality over the decades and centuries.
Re:The article is by Hannah Fry (Score:5, Insightful)
When you're out and about, what's the first thing you notice about a person? If you say anything other than their appearance, you're lying.
We (both men and women) immediately determine if a person is attractive or not based on their appearance because that is the first thing we see. We may not vocalize it (unfortunately, some do), but we notice. For the OP to indicate Dr. Fry is attractive in addition to her feats of mathematics and seminars is merely an additional bonus.
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Re: The article is by Hannah Fry (Score:3)
The fight against the sexualization of women has more to do with the quality and source of the sexualization than it does with the volume. Most women want to be sexualized in the right way by the right people. Unfortunately, without a published guide on how to properly sexualize her, the quality (benchmarked against the source) of most sexualization isn't going to meet her standards.
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Re:The article is by Hannah Fry (Score:4, Interesting)
Show me the last time a male scientist on video got a Slashdot comment "Wow, he's hot".
Re: The article is by Hannah Fry (Score:3)
Slashdot is going to be a pretty skewed data set. I've definitely encountered women using phrases like "Dr. Sexy" when they come across an intellectual with good hair and a solid chin.
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News for Nerds (Score:4, Interesting)
The real news for nerds is "Hannah Fry landed a New Yorker Article", congrats. Pity about the pay (or spam subscription) wall, I didn't get much beyond the author name and the TFS copypaste.
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the news for nerds is not that "Statistics is an exact science/helpful in many fields to guide decisions" we knew that already.
Have you seen the level of discourse on Slashdot recently? I think it's about time we get back to basics. The nerd is dead.
Re: News for Nerds (Score:2)
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Exactly,
Lets get back to the esoteric stuff that other news sources don't cover.
The general news, (especially Social Media and Cable based sources) are based on entertaining, not informing, the people. So when a news article reaches their desk they are going to spin it to a point where we are forced to get an emotional reaction from it, then their competing news source is going to spin the same thing to get an opposite emotional reaction from it.
If the topic isn't covered by these entertainment "news" site
Yep (Score:5, Interesting)
15 year ago, we had this case study on the first day of an MBA class. My first thought: don't race, the risk / reward tradeoffs are bad. Second thought: hey this sounds like what happened to Challenger. Most of us with an engineering background had the same reaction.
Among non-engineering students, not only did most fail to spot the connection. Most students were like "Wait, what happened to Challenger?"
Re:Yep (Score:5, Interesting)
For about a decade I worked with blood manufacturing software with a large blood bank
We used six sigma tools to monitor, identify and correct process failures because a mistake in any step of the donation, processing and delivery steps can KILL people
In order to do this we had to employ strict documentation and 'honesty' standards because statistical analysis is shit if people are not documenting problems or lying to you about them
That is where most companies fail when trying to use statistical analysis for themselves. GIGO Garbage In Garbage Out
imo, most organizatios
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oopsy
imo, most organizations fail to collect good data, so analysis is unlikely to produce usable results
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That is where most companies fail when trying to use statistical analysis for themselves. GIGO Garbage In Garbage Out
This is why I despise telemetry. It's not just a privacy issue. I refuse to believe it actually works, and companies just slurp up data because they can, it makes for nice reports, and gives managers a higher sense of knowledge and control then they really have.
Re: Yep (Score:3)
Maybe I'm just generally distrustful of the level of competence and honesty in graphs, but the first thing I do with a graph is parse the exact language and labels, check how the graph is framed (start/end date framing in economics, for example), and generally try to tear it apart.
Most people don't understand math or statistics well enough to create objective graphs. And I know full well it's usually going to be a graphics artist and not a statistician.
Graphs are in news stories primarily for morons who can
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Ha ha! Me too - in fact if I see any graph or chart of numerical data the does not include the 0,0 point (i.e. only showing from X1 -> X2 and Y1 -> Y2) I immediately assume it is trying to lie to me, and proceed from there.
Sounds like you have the same reaction.
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And after they learned probably thought, "Gee, they couldn't find a way to blame it on somebody else."
Why not go to the source? (Score:2)
Why not just assign _The Challenger Launch Decision_? My guess being that it is a 400 page book with footnotes and MBA candidates prefer everything in a simple chart, without the nuance of contributing factors such as organizational psychology, personal ambition, "don't bring me problems" cultures, etc.
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More to the point, their "blowout" scenario actually sounds like they're describing the Challenger Launch Decision, just thinly disguised.
Re:Why not go to the source? (Score:5, Insightful)
"Congratulations, you just killed seven people - because you blindly accepted incomplete data".
Re:Why not go to the source? (Score:5, Insightful)
It obviously IS the Challenger launch decision. But if you called it that, then gave students the data and asked for fly/no-fly, they would have to be complete morons to select 'fly', whether they understood the problems with the data or not. So they fictionalize it so the outcome is not already known.
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The Challenger disaster, as Feynman discovered, was more due to a culture discrepancy between the political management and the pragmatic engineers. Concerning the notorious O-Rings, the upper management was operating under a distorted notion of the term "safety factor" where, in fact, there was none.
Alas, what you describe is closer to what happened with Columbia. Here we have a case where the problem was known and investigated, but the ball was dropped at the last critical minute of decision-making where t
Only the failures? (Score:5, Insightful)
This sounds like the opposite of Survivorship Bias [worldwarwings.com].
Re:Only the failures? (Score:5, Informative)
+1
You beat me to it. I'll just mention Abraham Wald [wikipedia.org], who worked on this.
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A Matter of Life and Death Indeed (Score:5, Insightful)
I find it odd that the headline is about how pie charts are now being used in a "matter of life and death", with the example given being data presented in the context of racing cars. It's almost as if the writer of the article doesn't know that pie charts - specifically pie charts - were popularised by (if not invented by) Florence Nightingale; she decided it was the easiest way to communicate her statistical analysis of injuries, deaths and recovery rates in the Crimean War. Surely that *original usage* was far more a matter of life and death than the example given of how we are "now" using pie charts to determine how much money a company will make from a potentially dangerous event...
Re:A Matter of Life and Death Indeed (Score:5, Informative)
If you RTFA (I know, I know) you will find that the "race car engine" failure data were actually from Space Shuttle O-rings, and that the first data set graphed -- showing when failures occurred, but not successes -- was presented (in tabular form) the night before the Challenger launched.
The Fine Summary gives a terrible sense of how the article begins.
Re:A Matter of Life and Death Indeed (Score:5, Funny)
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Pie chart? I’m gluten intolerant you insensitive clod! Now let’s launch this puppy!
Damn - where are those mod points when I need them? LMAO!
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I was about to say. As I was reading that, my mind immediately went to Challenger’s O-rings, but I figured that it must have just been a coincidental similarity. I apparently haven’t had enough caffeine yet this morning to realize it was an intentional similarity formulated around that same time.
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If you RTFA (I know, I know) you will find that the "race car engine" failure data were actually from Space Shuttle O-rings, and that the first data set graphed -- showing when failures occurred, but not successes -- was presented (in tabular form) the night before the Challenger launched.
The Fine Summary gives a terrible sense of how the article begins.
I did not, but it seemed impossible there existed an engine gasket issue where it went bad before the engine started because it was cold. And that this was never detected, seemed odd in the extreme. Knowing this now it makes more sense. There were only a handful of data points, not hundreds as in racing car high end machinery, or millions in street cars.
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I found this slide presentation.
https://slideplayer.com/slide/... [slideplayer.com]
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I was wondering what was up with that summary. When have you heard about a car racing team skipping an event because they're afraid their car might blow up? I though that was the whole point of racing.
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Paywall, dude. The only thing worse than a bad summary is a bad summary of an article you can't read.
Personally, the only reason why I still visit Slashdot is because of the commentary, but if this place insists on making bad summaries of paywalled articles, even the commentary won't be worth reading anymore.
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SEND it. (Score:3)
Every other racer is going to have to endure the same weather conditions.
And who the hell, gets into racing, to find guaranteed profit? With the amount of money they blow out the tailpipe, it's a business expense and tax writeoff more than anything. You should have deep pockets to even dream of this hobby.
Go big or go home.
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Sounds like the space shuttle o rings (Score:1)
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Re:Sounds like the space shuttle o rings (Score:5, Insightful)
It is the Challenger. Not changed 'to protect the innocent', but changed so that the students being asked to look at the data don't already know the outcome.
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Problem is that changing the context changes the underlying assumptions. It is very unusual for a race car engine failure to risk anyone's life -- unlike a spacecraft a car will just coast to a stop when the engine fails. The tradeoffs and risks aren't even close to being the same.
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They state that the engine failure DOES risk the driver's life (and they also throw in a financial consideration), so that 'assumption' should not be made. And in any case, how does that change the fact that you are looking at incomplete data and shouldn't make a decision based on that?
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But everything in auto racing does risk the driver's life.
I remember this little exercise from a business class. When asked specifically about other data points (exactly the point the article is making) and quantification of the additional risk, the professor told us that this is all that we had available, then was smug when we made the "wrong" choice.
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I'm confused. You're saying you didn't have enough information, and you realized that, and you still chose the riskiest option?
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Abraham Wald - saving flight crews (Score:5, Informative)
The example I like to use is that of the mathematician Abraham Wald and his work on armoring planes. He asked a similar question. Before him planes that made it back were looked at and where the bullet holes were most frequent that's where the armor went. His method was "Those planes made it back. Let's think about the planes that didn't -- did they get shot where the bullets holes aren't on the returning planes?"
https://www.wearethemighty.com... [wearethemighty.com]
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Fight Club where he beat him self up office Boss (Score:2)
Fight Club where he beat him self up in the bosses office.
Ok to not tell any one about what I know my new job will be to just collect pay checks and not tell any one.
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Are u the guy with the moobs?
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that wasn't a guy, that was Meatloaf!
Well, duh... (Score:4, Insightful)
Contemporary machine learning technologies, e.g. gradient descent & linear and logistical regression as well as normal distribution and standard deviation of datasets that highlights important patterns of vast amounts of information are much more interesting.
Of course they are only as good as the quality and quantity of the underlying data.
Graphs are fantastic but if you don't... (Score:3)
have access to a graphing program there are other ways of understanding data that works (in some cases).
https://www.nytimes.com/2021/0... [nytimes.com]
In the NYTimes article "Who's Afraid of Big Numbers?" (sorry if there's a paywall), another way to understand (big) numbers is by scaling them down to "normal" (not Jeff Bezos) size. For example by setting the total U.S. revenue at $100,000 and all other numbers appropriately you can see that, for example, the Department of Defense spends $17,310 whereas NASA gets $549 (no political commentary there at all folks :).
Despite the fact that Graphs are making me(!) money because of my investment in a successful bioinformatics data visualization company, I have to admit that there are times when, as the NYTimes article shows, they are not always the best way of understanding data. Using the above as an example, if you were to plot how much money was spent on school meal programs ($5) it would be a tiny tiny sliver that you wouldn't even be able to see, whereas by presenting the data in terms of a household budget you could at least understand it.
Still, Graphing data can be incredibly useful and, if I may be so bold, is sometimes beautiful. May I refer you to a classic? Edward Tufte's "The Visual Display of Quantitative Information". Highly recommended :)
Just work for a republician administration (Score:1)
Pie charts usually suck..... (Score:1)
Pie charts have a lot of inherent issues. In many cases a bar chart is a much better choice, though pie charts can be useful if certain conditions are met.
For more insight into why pie charts are often a poor visualization choice, read Steve Wexler's recent book, The Big Picture.
pie charts (Score:1)
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Or do the bar charts make a big deal out of insignificant differences?
Very few graphs or plots in the article (Score:3)
This article is meant for humanity majors who like reading large blocks of text. There are much better articles and books that talk vividly about visual representation of large data sets, multi dimensional data etc.
The problem is making BAD graphs (Score:2)
The entire 'Carter' study centered on a bad graph. It could have shown both, with red being blow outs and green being good outcomes.
The major problem is caused not by bad graph reading, but by bad graph making. Too much work is put into making it simple and pretty, not enough into making it valuable.
My personal pet peeve is the lack of zero-axiing. If you line/mountain graph something with a high number, people tend to start the graph near the number, rather than at 0. This makes it easier to see the s
Shocking (Score:2)
Lies, damned lies, and statistics
Ten Years of Useless Metrics (Score:5, Interesting)
Before I retired a couple years ago, one of my functions was to prepare a monthly report with about 25 slides showing metrics for upper management. When I'd present these (as I was required), I'd occasionally get a question or two, but almost noting ever of any significance. Note that it typically took 15-20 hours of my own preparation, along with input gathered from a dozen customer sites where our employees had to typically spend a day gathering their data to send to me.
I made the case on several occasions to do away with this process, and was poo-pooed each time, even when mentioning the cost in time/money for something that wasn't used. On the plus side, I got to work from home when doing this, and it kept me employed through the recession.
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Before I retired a couple years ago, one of my functions was to prepare a monthly report with about 25 slides showing metrics for upper management. When I'd present these (as I was required), I'd occasionally get a question or two, but almost noting ever of any significance. Note that it typically took 15-20 hours of my own preparation
(emphasis added)
You were overdue for retirement, then. Automate the process FFS! I wrote R-code in less than your 15-20 hours to take the raw data and generate a full powerpoint report deck with one click. You can do the same using MATLAB toolboxes or even roll-your-own executables based on MsoftOffice APIs.
Re:Ten Years of Useless Metrics (Score:4, Interesting)
You're making an assumption that I didn't try to. I left out what I thought were unnecessary details.
This is part of the problem when you work for a F500 bureaucracy...funding, agreement to allow it to be done (it's the customer's data), etc. Our program's chief engineer had promised me he would get it worked on for well over a year...nothing. I also tried to offload the task to three others over the years, spending untold hours training each of them. By the time I'd punched out, we had gotten about half of the task semi automated with Excel macros, and then not long after I departed they stopped doing the job because nobody else could/would do it...it was not only a pain in the ass, it was thankless...honestly one of the worst tasks I had in 37 years with that company. I had ~50 people to manage at four customer locations, but couldn't give any of them the tasking because they were funded directly by customers, while this was for internal use. There's plenty more to the story, but I'm sure you'll find some other reason to fault me.
The challenger disaster reshaped my life (Score:3, Interesting)
This studio version of the song [taht.net] includes samples from richard feynman's famous demonstration of what had gone physically wrong and much more
In the song I firmly put the blame for the disaster on the management types that didn't understand science, and were too busy posing for the cameras or trying to please their bosses to notice what feynman and one brave engineer [wikipedia.org] were trying to say.
Over the last decade...
In working so hard [blogspot.com] to avert a worldwide bufferbloat congestion collapse disaster [bufferbloa...beyond.net] across the internet, and knowing damn full well that how the internet actually works is still so persistently mis-understood [apnic.net], in particular, I've pulled out all the stops to attempt to kill the L4S vs RFC3168 disaster still unfolding in the ietf, and I'd laid down another version [youtube.com] after successfully - at least temporarily - halting it, 2+ years ago. L4S is still alive and going into last call at the ietf, despite all the evidence gathered against it [github.com] to date, backed by some of the biggest firms in the industry.
I don't know if it's possible to "enjoy" this song, but when you've tried writing books, code, presentations, and screaming at the top folk running the internet to "do something", what else can you do? Have a listen.
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Have you considered a mix of MDMA and psilocybin?
Perhaps in a relaxed setting, like a mountain music festival... it does wonders for ptsd
Gave the song a listen, you really captured the pain/angst of an event that I remembered as signaling the end of an era, or at least my own optimistic ideas for the future
so yeah, find a music festival, make sure you have a lot of water and take a hippie flip, you will find comfort
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In the song I firmly put the blame for the disaster on the management types that didn't understand science, and were too busy posing for the cameras or trying to please their bosses to notice what feynman and one brave engineer [wikipedia.org] were trying to say.
Feynman wasnt brought in until after the disaster, and he wasnt reporting to NASA he was reporting to Congress.
They brought Feynman in not to get to the bottom of it, but to explain the mess to dumb Congressmen, because Feynman was the Greatest Teacher To Ever Live. The fact that Feynman figured out exactly what went wrong himself is just icing on the cake. Brilliant person. Its too bad you didnt do any research.
As the old saying goes⦠(Score:1)
There is a wonderful example of this in medicine. (Score:3)
Surprising Outcome (Score:2)
At least Johns priorities were in the right place (Score:3)
Engine failure "will jeopardize sponsorships -- and the driver's life"
I mean, who would risk sponsorships?
The TFA isn't quite so convincing (Score:2)
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One data point is likely NOT conclusive, but it points you in a direction to investigate
Let's say that you were able to interview a person involved in that single data point, and they revealed that the engineering staff had doubted that it would work in the first place, was outside of design specs and therefore not necessary to take additional datapoints in that range
So yea, gotta place Sherlock sometimes
Older story? (Score:1)
Sounds like the story of aeroplanes during WWII.
People were looking at where the ones that got back had been shot and so decided to add more armour there.
Until someone pointed out that these had got back - so it was the ones that were shot elsewhere on the fuselage that had been shot down and hence the place to increase the armour was where those that returned had not been shot.
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um, you may want to rtfa where it is revealed to be actual Morton Thiokol o-ring data for the boosters used on space shuttles
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Reading the article I realized there's no way the low air temperatures experienced at a race track
If you actually read the article, you'd have found the data was actually Space Shuttle O-Ring failures, and the graph was from the day the Challenger exploded.
The subject was changed so that the students wouldn't automatically leap to "no-go" based on knowing how it turned out.