Open Source Pioneer Michael Tiemann On the Myth of the Average 127
StewBeans writes: In a recent article, Michael Tiemann, one of the world's first open source entrepreneurs and VP of Open Source Affairs at Red Hat, highlights an example from the 1950s US Air Force where the "myth of the average resulted in a generation of planes that almost no pilots could reliably fly, and which killed as many as 17 pilots in a single day." He uses this example to argue that IT leaders who think that playing it safe means being as average as possible in order to avoid risks (i.e. "Buy what others are buying. Deploy what others are deploying. Manage what others are managing.") may be making IT procurement and strategy decisions based on flawed data. Instead, Tiemann says that IT leaders should understand elements of differentiation that are most valuable, and then adopt the standards that exploit them. "Don't aim for average: it may not exist. Aim for optimal, and use the power of open source to achieve what uniquely benefits your organization."
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It was submitted by someone who is way above average at shilling. All his posts are puff pieces for undieprisersprotests.
Plus ca change...
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It means you're a fat Alaskan trust-fund brat with a wrinkle fetish.
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Well I take it like a normal sales pitch. He is just happening to push Open Source software.
Do Great things, customized to your business, improve your work flow Open Source.
Do Grate things, customized to your business, improve your work flow SAS
The problem with Open source is the large projects that get all the attention and support and maintenance. Are usually the general purpose applications, not the specialized ones that people really need.
What is really needed is companies to bring back their own inter
Averages do exist (Score:3)
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I have learned that people get what they expect. Rarely more, often less. Average is simply settling for less than what you should expect.
Average of exceptional is still "average", if you keep your set exclusively "exceptional". Top ten athletes have an "average" among them, and if you're just looking at those ten, then "average" is still exceptional.
The problem with metrics such as these, is they are easily exploitable, either for benefit or harm. If you're staff is barely proficient, and all you look at
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In the past, I've mentioned how we didn't really have much in the way of formal job titles at the company I sold. We didn't have a lead programmer, we didn't have a specific QA team, and people have often asked me how that worked or what that looked like. I suspect they ask out of morbid curiosity but... Well, no... That's really what I expect.
Anyhow, what it looked like is that people found and filled their own roles. I'm speaking pretty much of the developer side of things. We didn't have a whole lot of "
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Oh, ha! I forgot to add... That's the power of the "average" person - I think. They can do pretty well for themselves if you get out of the way and let them do it. They do even better if you ensure that they've got the tools they want. (Lesson learned: That's NOT always the tools suggested by a vendor. When they say they want a certain compiler - get it for them. Err... We had proprietary compilers back in those days.)
At any rate, the "average" seems to be able to do okay if given the ability to do so. The
Re: Averages do exist (Score:5, Informative)
When managers deploy "average" security solutions, they're not trying to protect against threats, they're trying to avoid getting fired.
If they deploy something unusual and it doesn't work, they'll be fired, regardless of how it failed or the merits. If they deploy something everyone else has deployed and it doesn't work, they will be commended for following "industry best practices."
Not all organizations work this way, but many do. When something breaks, there's a big temptation to avoid an investigation into exactly what happened- who knows what that could turn up! Much easier just to fire middle managers for prima facie reasons.
Same thing happens in dating (Score:5, Interesting)
They get 3 people - one of which is married, one gay, and the other refuses to date someone as tall/fat/stupid/poor as them.
People just don't understand the selectivity of multiple and requirements.
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People do things like say "I only want someone with > average height, > average salary,
Clueless people often say things like that. What they mean by that is "I have no idea what I want", which is exactly what they will get.
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I'm pretty sure love needs to be somewhere prominent in this equation, others however may be content with their precious.
In any case, imposing restrictions on someone you haven't met is a sure sign of undealt perso
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If you are looking for someone with no 'undealt' personal issues, may I suggest you check Narnia, Atlantis, and Krypton - because you aren't living in the real world.
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Average? Pfft... most peoples' dating standards are too high.
I have two: 1) I have to be able to lift her, and 2) I have to be able to eat when I look at her.
(Note I said dating and not marriage.)
Please Explain (Score:4, Insightful)
Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
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Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
Apparently it had something to do with cockpit dimensions. Basically an inexperienced lieutenant fresh out of college tried to be slick. While I have never served in the military, all of the memoirs and personal accounts I've read point to that being a rather common occurrence unfortunately.
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No one seems to have pointed out this portion of TFA:
"During a time of great technical innovation in aviation — when fighters planes became fighter jets, and later super-sonic fighter jets — the design of the cockpit became more and more decisive in determining whether a pilot could stay ahead of the plane and successfully execute the mission, or whether the plane would get ahead of the pilot and fly out of control."
The nature and reliability of how this was determined is left unexplained, but i
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Your surety is in error. You are talking about the Vietnam war, a decade later. US air to air victories over the North Koreans were something like 10:1.
Re:Please Explain (Score:5, Informative)
FTA: "Using the size data he had gathered from 4,063 pilots, Daniels [Lt. Gilbert S. Daniels, who majored in physical anthropology at Harvard before joining the Air Force] calculated the average of the 10 physical dimensions believed to be most relevant for [optimal cockpit] design, including height, chest circumference and sleeve length. These formed the dimensions of the “average pilot,” which Daniels generously defined as someone whose measurements were within the middle 30 per cent of the range of values for each dimension. So, for example, even though the precise average height from the data was five foot nine, he defined the height of the “average pilot” as ranging from five-seven to five-11. Next, Daniels compared each individual pilot, one by one, to the average pilot.
Before he crunched his numbers, the consensus among his fellow air force researchers was that the vast majority of pilots would be within the average range on most dimensions. After all, these pilots had already been pre-selected because they appeared to be average sized. (If you were, say, six foot seven, you would never have been recruited in the first place.) The scientists also expected that a sizable number of pilots would be within the average range on all 10 dimensions. But even Daniels was stunned when he tabulated the actual number.
Zero."
Re:Please Explain (Score:5, Insightful)
One more reason at least basic probability should be taught as a high-priority subject, starting in high school at the latest. Probability is after all something that we all deal with almost constantly in informing our decisions, and by nature we're actually pretty lousy at assessing it.
If there's a 30% chance that someone will fall within the designated "average" range on any given dimension, and assuming the dimensions are roughly independent, there's only a roughly 0.30^10 = 0.0006% chance that someone will fall into that range for all dimensions. That's only one individual in 169,350. Nobody with the most basic grasp of probability would expect many people to qualify.
And these are scientists that expected otherwise? They should be ashamed of themselves.
Re:Please Explain (Score:4, Insightful)
And here you made a basic mistake: the assumption of dimensions being independent is obviously false. Someone tall usually has long hands, and so on. Most dimensions have a rather high correlation.
Re:Please Explain (Score:5, Interesting)
Very true, thinking on my post I was actually hoping someone would bring that up. As you say it's obviously false that there's no correlation; however, unless you've actually collected the data to know what the actual degree of correlation is, assuming independence gives you a useful lower bound on probability to sanity-check your assumptions. You could also run the numbers for some "reasonable" guesses at correlation:
For example - lets say you only select individuals who have the first dimension within "acceptable" range - 100% of them, so we can ignore it. Furthermore we'll assume that all other dimensions have a freakishly high 80% correlation rate with the first of falling within their respective "average" range as well. You still end up with only 0.8^9 = 13% of candidates having all 10 dimensions within the average range.
So even with freakishly optimistic assumptions about correlation, you *still* end up with fewer than 1 in 8 candidates having all ten dimensions within your desired range.
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If there's a 30% chance that someone will fall within the designated "average" range
Why would you assume there's only a 30% chance of that someone would fall within the middle 30 per cent of the range? That would only be true if we assumed for some reason that people would be uniformly distributed throughout the range.
and assuming the dimensions are roughly independent
Given that the dimensions all concern the physical size of the candidates I don't see why anyone would expect them to be independent. Someone that is very tall would, I assume, also be quite likely to have a long sleeve length for example.
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As I replied above in more detail, true they're unlikely to be completely independent; however, it still gives you a reasonable lower bound for sanity-checking your assumptions, especially in the complete absence of hard data on actual correlation rates. Furthermore, even assuming a freakishly high correlation rate still results in very few individuals falling in the ranges on all dimensions.
As for the 30% range - I agree that one is open to interpretation. I assumed they meant the 30% probability range.
Average == middle thirty percent? (Score:2)
If there's a 30% chance that someone will fall within the designated "average" range
Why would you assume there's only a 30% chance of that someone would fall within the middle 30 per cent of the range?
What the author said was:
...the “average pilot,” which Daniels generously defined as someone whose measurements were within the middle 30 per cent of the range of values for each dimension.
Depends on what you mean by "middle 30%". I assume that this meant "the range of values within which 30% of the population falls." I suppose it could, alternately, be interpreted as meaning "a pilot whose measurement falls at a value of the average plus or minus 15%."-- that is, if the average height is
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That's only one individual in 169,350. Nobody with the most basic grasp of probability would expect many people to qualify.
This might be part of the strategy. I don't think many people are expected to qualify as Air Force pilots anyway, so this is an easy culling measure.
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While limiting the number of applicants is useful, doing so based on arbitrary size requirements is a very silly way of doing it. If you want to limit your candidate pool you should at least use a measure that increase the odds of getting someone with some desirable trait. Which is probably why one of the first requirements to be an officer in all branches is a college degree.
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If you want to limit your candidate pool you should at least use a measure that increase the odds of getting someone with some desirable trait. Which is probably why one of the first requirements to be an officer in all branches is a college degree.
This seems equally arbitrary. I know plenty of smart, natural leaders who never finished University (and if you need independent references, Steve Jobs, Bill Gates etc). In fact I'd go so far as to say that in the last couple of decades, University seems to churn out more mindless robots than leaders these days.
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What I take issue with is that the arbitrary size measures they used didn't have any expectation of improving the quality of the candidate. I agree that the college degree is also arbitrary but it does set a baseline for some desirable skills/traits, even if that baseline is much lower than some would like to think.
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Somehow I think they would want more than 1,771 potential pilots from the entire US population. Especially since a lot of those will be too old, too young, or disqualified for psychological or other reasons.
Re:Please Explain (Score:4, Interesting)
>So, all of common sense goes against probability.
To a very large extent, yes. Which is exactly why understanding the implications of basic probability is so important to behaving in a rational manner.
This isn't even anything complicated, venture into the wild and woolly world of false positives and the math only gets slightly more complicated, but the implications become radically less intuitive. For example testing positive for a rare disease on a test with only a 1% chance of a false positive, still means you almost certainly don't actually have the disease. (if 1 in 1000 people have the disease, then out of an average 1000 people, 10 will test positive, but only 1 will actually have it. A 90% chance that you're clean.)
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Variability in averages when the set is large enough. Average is meaningless when dealing with a specific need.
In this case, an "Average" pilot didn't actually exist.
Or, as my dad used to say, "In theory, theory and practice are the same, in practice, they are not". "Average" is Theory, and practice is measuring each pilot and discovering that none are in fact "average".
Re:Please Explain (Score:5, Insightful)
But even Daniels was stunned when he tabulated the actual number.
Zero."
Which has actually jack squat explanation as to why the pilots died.
For all the details we are given the dead pilots could have been flying bespoke aircraft with hand crafted cockpits that matched there personal ergonomics, but were flying against enemies with aircraft that were 10 times superior in flight ability, but were mass produced.
Without any further details, the idea that ergonomics killed those pilots is pure assumption.
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My uncle is 6'2 and flew reconnaissance F4 Phantoms in Viet Nam. Just what is the height constraint?
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My grandfather flew B-52s in Korea. When he was up for the SR-71, it was deemed that the length of his legs was too great; he couldn't use the rudders or fit in the cockpit.
My grandfather is also quite tall, not sure exactly how tall, but his height wasn't even a concern, just the length of his legs was an issue.
It happens that different jets have different constraints on the dimensions of the pilots.
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My uncle was on B-52s as well before that.
One of the B-52s he flew is now on display outside the Orlando airport.
When he and my did did a tour of the boneyard at Davis-Mothan air base, one of the B-52s on the tour was another B-52 he flew on.
Height constraints (Score:3)
My uncle is 6'2 and flew reconnaissance F4 Phantoms in Viet Nam. Just what is the height constraint?
http://work.chron.com/air-forc... [chron.com] states:
Pilots have to meet the Air Force’s height, weight and physical conditioning requirements. They must be 64 to 77 inches tall when standing, and 34 to 40 inches tall when sitting...
77 inches is 6 foot 5 inches, so looks like your uncle made it with three inches to spare.
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Ergonomics Kill
That would make a great bumper sticker.
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That number by itself only tells part of the story, the next significant measurement of the pilots measurements was that a full 80% fell well within one standard deviation of those 70% on at least 50% of their respective measurements.
At this point most people are starting to
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From the description of the problem the only issue i can imagine is them is making the design so exact, and not building in any adjustability at all, that pilots with shorter than average arms would have trouble reaching the controls and pilots with shorter than average legs would have trouble reaching the pedals and etc. However i find it hard to believe that anyone, even the military, even when aggressive
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If you look at the article that is quoted in the quoted article, it doesn't explain how bad design killed the pilots either. But it does say that it was only after the scientist pointed out that no one fit the "average" dimensions did the military start ordering planes with adjustable seats, adjustable pedals, etc. So apparently they did previously build the planes with no adjustment capabilities.
Re:Please Explain (Score:5, Funny)
Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
Zero."
"... at which point he flew into a homicidal rage, killing seventeen of the pilots before he could be subdued and sent to a mental institution."
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FTA: "Using the size data he had gathered from 4,063 pilots, Daniels [Lt. Gilbert S. Daniels, who majored in physical anthropology at Harvard before joining the Air Force] calculated the average of the 10 physical dimensions believed to be most relevant for [optimal cockpit] design, including height, chest circumference and sleeve length. These formed the dimensions of the “average pilot,” which Daniels generously defined as someone whose measurements were within the middle 30 per cent of the range of values for each dimension. So, for example, even though the precise average height from the data was five foot nine, he defined the height of the “average pilot” as ranging from five-seven to five-11. Next, Daniels compared each individual pilot, one by one, to the average pilot.
Before he crunched his numbers, the consensus among his fellow air force researchers was that the vast majority of pilots would be within the average range on most dimensions. After all, these pilots had already been pre-selected because they appeared to be average sized. (If you were, say, six foot seven, you would never have been recruited in the first place.) The scientists also expected that a sizable number of pilots would be within the average range on all 10 dimensions. But even Daniels was stunned when he tabulated the actual number.
Zero."
You just quoted the article, that doesn't explain at all HOW bad cockpit dimensions killed 17 pilots in one day.
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It is interesting that this currently rates as '4, informative', given that it does not explain a single fatality. I am curious as to how anyone could think this is an explanation, as that might explain a lot about the current state of rational thought.
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One purpose of an explanation is to be a better alternative to an assumption. If you had written something like that in your original post (assuming you're the same AC), then you would have had something beginning to look like an explanation.
Standard deviation (Score:2)
It sounds like he just made an average and called it that. That's bad math. Standard deviation shows how far your average can vary.
For those of you who don't do math.
Values of 90,91,92,93,94 brings a standard deviation of 1.58 and values of 88,90,92,94,96 brings a standard deviation of 3.16. Same average of both groups, but the standard deviation shows how far out your variances go from average.
Re:Please Explain (Score:4, Funny)
Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
Software debugging methodology. It killed one pilot and management said to run it again and see if it does the same thing.
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Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
Yeah, some more detail there would have been nice, but as a pilot, I can easily see how this was certainly related to physical dimensions. Consider the non-average pilot with shorter than average arms. If he lost his grip on the stick during certain maneuvers, it's entirely possible that it could have moved to a position out of his reach. Same goes for rudder pedals for the pilot whose knees won't fit under the panel when he's thrown forward in the straps, or that won't allow them to bend enough to allow fu
Re:Please Explain (Score:5, Informative)
The quoted and linked article with the original article explains it a bit.... Here is a summary... When we started making jet fighters we had lots and lots of crashes, but they weren't from mechanical issues, so that seemed like pilot error, but the pilots didn't really think they were doing anything wrong. The cockpit design they were using was from 1926 and based on un-adjustable controls with positioning calculated from the "average pilot". While the upper management at the military was arguing about the costs of redesigning the planes engineerings invented adjustable seats and controls and pilots stopped crashing so much.
Myths aren't real (Score:2)
Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
No, you didn't miss it because it wasn't there.
The definition of a "myth" is a good story that didn't really happen. This seems to cover this alleged airplane: it's a nice myth, but don't expect it to be real.
Re:Please Explain (Score:4, Informative)
Did I miss the part of the story that explains HOW it managed to kill 17 pilots in one day?
The book, The End of Average by Todd Rose was misquoted.
First of all the exact quote from the first paragraph of the book was this:
At its worst point, seventeen pilots crashed in a single day
There is a huge difference between crashing and dying.
Anyway, he (Teimann) got the sequence of events wrong, but the general gist of what he said follows the intent of the book.
The crashing planes in the study were the in the 1940's. We're talking about planes like the P-80 and possibly the F-86. That was the first generation of jets and they had many many problems in design.
Here's where the average pilot comes in. Those planes had been designed for the average pilot's size as measured in 1926. The cockpit was non-adjustable, so The Army/Air Force sought pilots whose size fit the planes, but only that person who matched the average 1926 pilot would fit properly. In the highly demanding jets of the late 1940's, a pilot that didn't fit could have problems when split second control reactions were needed, and those planes needed it.
The study conducted by Lieutenant Gilbert Daniels in 1950 which examined modern average pilot sizes, was completed in 1952. The upshot of that study was that the Air Force immediately decided to take the study's recommendation: Everyone is different, and to get the maximum performance from people you adjust the environment to the soldier, not the soldier. The Air Force immediately mandated that the manufacturers make many elements of the cockpit be adjustable for the range of sizes from 5% to 95% of men from the seats, to pedal positions, to belts, and helmet straps, and so on. The result was that pilot performance soared and the US Air Force became the most dominant air force on the planet.
The book gives other example studies and goes on to say
Any system designed around the average person is doomed to fail
This is the gist of the book and what Michael Tiemann was getting at.
Anyway, the summary implied that the generation of planes designed in the 1950's were a generation of pilot killers.
This is wrong, the book said the opposite. The 1950's planes had the cockpit fit problems solved.
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Fantastic explanation, thank you!
In other words, quit buying Red Hat Linux (Score:4, Interesting)
>> Red Hat VP: IT leaders who think that playing it safe means being as average as possible in order to avoid risks (i.e. "Buy what others are buying. Deploy what others are deploying.")
Why isn't this article entitled "Red Hat Linux executive tells the sheeple to quit buying Red Hat Linux - there are plenty of identical and cheaper alternatives available?"
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Why isn't this article entitled "Red Hat Linux executive tells the sheeple to quit buying Red Hat Linux - there are plenty of identical and cheaper alternatives available?"
No, it’s more like: Buy Red Hat. They employ the largest number of influential Linux hackers (Poettering, Sievers, Molnar, etc.), so you can be assured that open source stuff works best in a Red Hat distribution.
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For variable values of "best". If I look at systemd, which would at least need another few years of time and some lead developers with an actual clue about Unix to be ready for prime time, I highly doubt that Red Hat is a good choice for anything.
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Why isn't this article entitled "Red Hat Linux executive tells the sheeple to quit buying Red Hat Linux - there are plenty of identical and cheaper alternatives available?"
Because it wouldn't be true?
Of the myriad changes found in RHEL 7, a few are certain to cause consternation. First and foremost of those is the move to the Systemd system and process manager. This represents a major departure from Red Hat's -- and Linux's -- history and from the tried-and-true Unix philosophy of using simple, modular tools for critical infrastructure components. Systemd replaces the simplicity of Init scripts with a major management system that offers new features and capabilities but adds significant complexity.
Both sides of the Systemd divide have their adherents, but in RHEL 7, the Systemd argument has clearly won. I believe, however, that this will ultimately rankle many veteran Linux admins, and we may be on the road to a real schism in the RHEL community and in the Linux world at large.
Review: RHEL 7 lands with a jolt [infoworld.com] [August 2014]
One KEEPS working and solving new problems. Diced (Score:4, Interesting)
> open source over proprietary ...
>
> Choose software because it works and solves a problem for you, not just because it is open source.
Proprietary software can get Diced, and there's a good chance that'll eventually happen to the one you choose.
At my last job, the first major project I was assigned to was replacing some proprietary software with open source for one of our critical systems. The proprietary system was originally chosen because it looked liked it worked (in vendor demos) and it seemed like it would solve most of the problems it needed to solve. Once it was actually used in production, they found that it mostly worked, and kinda solved a lot of problems, but created new problems. The vendor wasn't too enthusiastic about fixing things and certainly wouldn't add needed features because they were (once again) moving on to their "next generation platform". After a few years, our needs changed a bit, new requirements came up, and the old proprietary system really wasn't working well at all.
We downloaded the most recent version of an open source solution, called Moodle, and took a few minutes to set it up. Rather than watching the vendor's sales people demo it, we could actually try it out, and try loading our actual data into it. It actually worked with our real data, and could be integrated into our other systems. An idiosyncrasy or two was handled by adjusting the appropriate line of code and submitting the fix/improvement back to the FOSS project. After it went live in production, departments asked "can it do this? It would be great if it could do that.". For each feature, it it didn't already have the feature, I took a few hours to write a little plugin implementing the requested feature. A few years later, I'm even more confident FOSS was the right choice - it's basically impossible to have a major roadblock with the software because in the worst case we could just add a little plugin to have it do whatever we want.
In the very worst case, the project -could- completely change direction, and the organization using it could just keep using the version that already works well for them, applying any commits they want from the new version.
The best that can be said about any proprietary software you're thinking about adopting is that it looks like it will handle your needs, for the moment. Open source will continue to handle whatever needs come up, given that you form a relationship with either the sponsors of the project or any programmer of your choosing to handle the plugins and patches that you want to have as the need arises.
For that reason, open source is objectively better and more reliable on the "solves problems" and "it works" metrics, because it'll continue to work next year - it won't be dropped when the vendor is sold to Dice.
Of course, as you said, there is such a thing as bad proprietary software and bad open source software, and you want to avoid choosing bad software. Given to pieces of software that appear to be roughly equally good _at_the_moment_, the open source is better because the risk of insurmountable problems down the road and vendor lock in is essentially removed.
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Proprietary software can get Diced, and there's a good chance that'll eventually happen to the one you choose.
That isn't so! We just ported all our stuff to Windows 8 and re-trained all of our employees. It's a good system and will be around for the foreseeable future .... [Just a sec. Gotta deal with all these 'Upgrade Now!' popups].
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No, he's the VP of Open Source Affairs.
Although, when you have a VP at Corporate HQ in charge, that probably takes all the fun out of having an affair.
80/20 (Score:4, Interesting)
For every 1 person that reinvents the wheel, 9 others use an existing wheel for the wrong job or misconfigure the wheel because they don't understand their problem well enough. If you truly understand your current issue, you're smart enough to create a solution. Every time someone treats a tool like a black box of magic, looking at you programmers blindly using libraries without understanding how they work, it's because they don't understand the problem they're trying to solve.
P.S. Understanding what something is doing does not mean you know the exact details of the implementation.
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Having recently been a consultant at a company doing this ... that was their entire new strategy.
They were doing more with less, it was the new way forward, it was going to change the world, 80/20 was the new standard, no more of the white glove service, we'd focus on core functionality only and stop catering to the outliers.
Everyone not in management understood that to mean we're cutting budgets, and we're going to claim servic
Why pick popular (Score:4, Insightful)
Software that is popular with the most users is also the software that is least likely to be orphaned, leaving you to either keep obsolete machines running or else having to migrate some obscure data format into some different form.
Also, the most popular software is more likely to have the most annoying features "corrected" because so many users complain. (not to mention it has the most people posting work-arounds on the web for the things that don't work.)
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Before or after being infected with Zika?
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I heard someone refer to one of his coworkers as a "Zika baby". That didn't take long.
Averages are misleading (Score:5, Informative)
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It's great to hear that I am above average in at least one respect !
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And one ovary!
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For example, the average person has approximately 1 testicle.
Whenever people trot out the tired statistic that 70% (or whatever) of drivers believe they're better than the average driver, to mock the idea of how well people rate their own abilities, I trot out the statistic that 99.9% of humans have more than the average number of eyes. We assume gassian distributions with no idea if that's actually representative of the results.
With that said, at least the air force example is a case of misunderstanding of how averages concatenate, which is a slightly more complica
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Serves to show that on average, statistics are not a very good model of reality.
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Yes, the "average" bimodal distribution averaged has one hump.
Also, "peak X" has exactly one hump—subtype lumpy—for any proposed commodity X.
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And my comment is Godwin'd in a little over 2 hours.
Nice.
Job related (Score:2)
"Buy what others are buying" is good for getting experience with what others are using, which is useful if you think you might someday need a job somewhere.
A bit offtopic but... (Score:2)
Is it me or is timothy the only who posts stories now? Did the rest of the /. editors get removed with the acquisition?
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Is it me or is timothy the only who posts stories now? Did the rest of the /. editors get removed with the acquisition?
Yes. And nary a dupe has been posted since.
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The average Slashdot editor posts politically motivated crap with poor summaries, no fact checking, late, no spell checking and dupes. timothy was the only one left that fit the all the parameters.
the good is the enemy of the best (Score:2)
Main problem... (Score:3)
Determining what's optimal is hard. Determining what everybody else is using is easy.
Neat, but back in the real world people don't know (Score:2)
If it's really at the core of your business, then sure you should know. But for everything else I still haven't seen a single case where a business buying non-trivial software really knew whether or not you could fulfill all your business requirements before committing. In many cases it was even recognized that you're just looking for a tool that's good enough and try to make it work for you. Like I know every component in my computer. I can barely remember the brand of my washing machine. Now obviously I n
In business, adapt the tool via module/ plugin (Score:2)
Of course requirements change over time. When the current solution doesn't quite fit the new requirements, you get / write a module / plugin or patch to handle it. Unless you were short-sighted enough to make your business dependent on proprietary software that you're not allowed to adapt to your needs. If so, then yeah you have to start over with a new solution. Hopefully the second time you choose a modular open source solution so that you aren't in the same jam 12 months later.
Skip this, read the article it references. Really (Score:3, Insightful)
The article misquotes an excerpt from a book here:
http://www.thestar.com/news/insight/2016/01/16/when-us-air-force-discovered-the-flaw-of-averages.html
This explains more of the story: the measurements were originally taken in 1926, but it wasn't until the 1950's that increased speeds for fighters made the design flaws apparent. The 17 deaths is an agile enterprise adaptation of 17 non-fatal crashes. Anyhow, it seems intuitive that body measurements would be correlated, so I'd say the big error was not checking that assumption. Kind of amazing bad science lasted that long.
Oh, he's missing the point (Score:3)
possibly intentionally.
The reason people are so conventional is what economists call "agency costs". They aren't minimizing risk to their employers, they're minimizing risk to themselves.
Book misquoted; pilot crashes were in 1940's (Score:5, Interesting)
The book "The End of Averages" by Todd Rose was misquoted
First of all the exact quote from the first paragraph of the book was this:
At its worst point, seventeen pilots crashed in a single day
There is a huge difference between crashing and dying.
Anyway, he (Teimann) got the sequence of events wrong, but the general gist of what he said follows the intent of the book.
The crashing planes in the study were the in the 1940's. We're talking about planes like the P-80 and possibly the F-86.
That was the first generation of jets and they had many many problems in design.
Here's where the average pilot comes in. Those planes (the 1940's) had been designed for the average pilot's size as measured in 1926. The cockpit was non-adjustable, so The Army/Air Force sought pilots whose size fit the planes, but only that person who matched the average 1926 pilot would fit properly. In the highly demanding jets of the late 1940's, a pilot that didn't fit could have problems when split second control reactions were needed, and those planes needed it.
The study conducted by Lieutenant Gilbert Daniels in 1950 which examined modern average pilot sizes, was completed in 1952.
The upshot of that study was that the Air Force immediately decided to take the study's recommendation:
Everyone is different, and to get the maximum performance from people you adjust the environment to the soldier, not the soldier.
The Air Force immediately mandated that the manufacturers make many elements of the cockpit be adjustable for the range of sizes from 5% to 95% of men from the seats, to pedal positions, to belts, and helmet straps, and so on. The result was that pilot performance soared and the US Air Force became the most dominant air force on the planet.
The book gives other example studies and goes on to say
Any system designed around the average person is doomed to fail
This is the gist of the book and what Michael Tiemann was getting at.
Anyway, the summary implied that the generation of planes designed in the 1950's were a generation of pilot killers.
The 1950's planes had the cockpit fit problems solved.
The crashing planes were in the late 1940's. The study was begun in 1950. Obviously, those crashes were not combat-related. Those planes were demanding and possibly evil, and a bad-fitting cockpit made it worse.
Re: (Score:2)
This seriously annoys me. I've heard management call for us to do more with less, be above average or seek the best solution and then require us to implement "best practices." How can you not see the contradiction with that.