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I post stupid ****
Fixed
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Which Big 12 member is the team you most want to beat once we join?
- Thread starter Buffnik
- Start date
I think I'm still gonna hate Utah the most. Those ****ers def think they're gonna rule the roost in the new Big 12. We gotta put a stop to that right quick.
I understand the vote for ****bailer, they can eat ****, but I am going to single handedly force a CU-BYU rivalry if I have to.
Come on! Utah can't even make weird underwear jokes because most of them wear them too! BYU needs to have lame dickheads take cheap potshots at their weird and pasty ways in this conference and we're just the assholes to do it!
I understand the vote for ****bailer, they can eat ****, but I am going to single handedly force a CU-BYU rivalry if I have to.
Come on! Utah can't even make weird underwear jokes because most of them wear them too! BYU needs to have lame dickheads take cheap potshots at their weird and pasty ways in this conference and we're just the assholes to do it!
Oh yeah. Them.Cincinnati. Obviously.
But as far as Utah goes, I hate them more now than I ever did during our entire time in the P12. They are probably second compared to Baylor.
Only 55% for **** bailer?
Get your **** together, people!
Get your **** together, people!
Lol, really? That's an interesting hate school. How come?
Person I despised was overly proud of their degrees from ISU
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Yeah. I hadn't known anyone who hated the Clones. Figured there were probably some Hawkeye & KSU fans who did due to rivalry, but I've only encountered decent, reasonable folks from the ISU fanbase.
Legit reason to hate a school, but I have to admit that the idea of anyone hating ISU drew an initial chuckle from me.
SeanMSan
Well-Known Member
Prime isn’t your typical coach…nor would he be a typical AD.Stoptrying
Out running today, I flashed back to this conversation.
I don’t think “incorrect” can be included in the definition of “precise”.
Let me expand. Incorrect is not included in the definition of precise, however, something can be precise and incorrect. And being inaccurate does not preclude something from being precise.
It's a common misunderstanding between accuracy and precision. Expanding on the pi example may help you.
| If I say pi is approximately equal to... | That answer is... |
| 3.14159265358979 | Precise and accurate |
| 2.06127651552485 | Precise, but not accurate |
| 3 | accurate, but not precise |
| 2 | neither precise nor accurate |
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Haha. Wow. That was like 7 months ago.
My counter argument would be that you can be correct but not precise. But you cannot be precise without being correct (otherwise it’s all just randomness…which is the opposite of precise).
adj
1. strictly correct in amount or value: a precise sum.
2. designating a certain thing and no other; particular: this precise location.
3. using or operating with total accuracy: precise instruments.
4. strict in observance of rules, standards, etc: a precise mind.
My counter argument would be that you can be correct but not precise. But you cannot be precise without being correct (otherwise it’s all just randomness…which is the opposite of precise).
precise
(prɪˈsaɪs)adj
1. strictly correct in amount or value: a precise sum.
2. designating a certain thing and no other; particular: this precise location.
3. using or operating with total accuracy: precise instruments.
4. strict in observance of rules, standards, etc: a precise mind.
Big Jim
WTF?
Right now, grammar nazis are looking pretty good.
Sounds like Hokie was in his happy place to be precise.
How would you categorize 3.14?
I don't know (remember) the context for this, but my initial impression is that by omitting that, you are being deliberately misleading.
In other words, where is "accurate and sufficient?"
For instance "**** bailer" is sufficient and accurate, though "**** Baylor University" would be accurate and precise.
Saying π ~= 3.14 is accurate, and more precise than 3, but less precise than 3.14159.
The question of whether or not 3.14 is "sufficient" comes down to how many significant digits the other measurements have, in this case the radius. If you measure the radius to three or less significant digits, 3.14 is sufficient. If your measurement system of the radius goes to five, 3.14 not sufficient to avoid losing precision in calculating the circumference.
The difference between precision and accuracy is taught in basic laboratory sciences.
The definitions used there, and in all the statistics I've engaged with, deals with measurement error.
For repeated measurements of the same characteristic, precision is how close each of the measurements are to each other and accuracy is how close the measurements are, on average to the true value.
If you're 180 pounds and have a cheap bathroom scale which you step on and off of 5 times and get measurements of 184, 178, 181, 175, 182 you'd call that scale accurate, but imprecise. On average it gets it right, but it's a scattershot of values.
Now you go to a doctor's office where the physician tried to calibrate the expensive scale hisself, but kind of messed up. You take five measurements and get 177.1, 176.9, 177.0, 177.0, 177.1. That would be a precise but inaccurate measurement.
The frequently used analogy in teaching is hits on a target. A tight grouping on the bullseye is precise and accurate, a loose grouping that surrounds the bullseye is accurate but imprecise, a tight grouping off the bullseye is innacurate and precise, a loose grouping skewed away from the bullseye is innacurate and imprecise.
File:Accuracy and precision.svg - Wikipedia
Accuracy and precision - Wikipedia
Where Aviator and I are differing is on a linguistic vs a scientific definition of the term. He's correct that in everyday speech that hitting the target is required to be precise. But in physics, the definition of precision is independent of accuracy.The difference between precision and accuracy is taught in basic laboratory sciences.
The definitions used there, and in all the statistics I've engaged with, deals with measurement error.
For repeated measurements of the same characteristic, precision is how close each of the measurements are to each other and accuracy is how close the measurements are, on average to the true value.
If you're 180 pounds and have a cheap bathroom scale which you step on and off of 5 times and get measurements of 184, 178, 181, 175, 182 you'd call that scale accurate, but imprecise. On average it gets it right, but it's a scattershot of values.
Now you go to a doctor's office where the physician tried to calibrate the expensive scale hisself, but kind of messed up. You take five measurements and get 177.1, 176.9, 177.0, 177.0, 177.1. That would be a precise but inaccurate measurement.
The frequently used analogy in teaching is hits on a target. A tight grouping on the bullseye is precise and accurate, a loose grouping that surrounds the bullseye is accurate but imprecise, a tight grouping off the bullseye is innacurate and precise, a loose grouping skewed away from the bullseye is innacurate and imprecise.
File:Accuracy and precision.svg - Wikipedia
en.m.wikipedia.org
Accuracy and precision - Wikipedia
This is a frequent problem in our language (maybe every language?) -- nearly every word has multiple definitions and often they are in conflict.
I've always owned it
In everyday language, being too precise actually distracts from meaning and/or intention.
I don't know if there's an equivalent phenomenon in science.
interesting observation -- not unusual for you to give me a new perspective to consider.
I will note, without snark, that in spoken communications, I generally favor being less precise, but it's more out of a concern of being accurate. When speaking, I'm generally communicating more rapidly, which means I'm giving less thought to what I'm saying -- this leads me to err on the side of "I'd rather be truthful than be specific and I'm willing to sacrifice clarity to reduce likelihood of speaking incorrectly". Your comment has me considering another aspect of that.
I vaguely recall some pedagogical research about overly precise language and jargon reducing the effectiveness of communicating and learning.
This also brings to mind the concept of model parsimony, where you can technically improve how well your model fits a set of data but tend to introduce some loss in prediction accuracy by adding more predictor variables, so you prefer the fewest number of inputs that gives you an adequate output.