Numbers to think about

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

???

Tell it again ....

It is not the way I learned math ; )

If the test are 99 % accurate (positive or negative) so 1 % (positive or
negative) are not.

If 380 came back positive and 1 % are not accurate, it is to say that 3.8 ,
let say 4 are not.

1.05 % are not accurate, it is to say 4 out of 380

Where you 1 of 3 comes from ?????

CowPunk said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

It's been a million years since I took a probability class so I must
have just confused myself. Someone please straighten me out here.

If the probability of a false positive is .01 then the probability of
both A and B samples receiving a false positives is .01 * .01 = .0001.
I think that means that ~1.2 times a year someone innocent should fail
both the A and B sample despite being clean.

That's got to be wrong.

1% of 12000 = 120

120:380 ~ 1:3

"Tim Lines" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

CowPunk said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

It's been a million years since I took a probability class so I must have
just confused myself.

I too

Someone please straighten me out here.

Quoted message said:

If the probability of a false positive is .01 then the probability of both
A and B samples receiving a false positives is .01 * .01 = .0001. I think
that means that ~1.2 times a year someone innocent should fail both the A
and B sample despite being clean.

That's got to be wrong.

Let put it in other way.

We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120.

In the 120 we have some Good Guys wrongly called cheaters, and some cheaters
called Good Guys

OK ?

Let see the distribution of this mistake :

380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called cheaters)

12000-380= 11620 Negative * 1 % = 116.2 (so 116.2 are cheaters but found
Good guys.)

Let see if there is a mistake . 116.2 + 3.8 = 120

Ok 120 is what we expected.

In short around 4 out 380 or 1 out 95 are poor guys called cheaters but they
are not..

On other side 116 out of 11620 or around 1 out of 100 are lucky cheaters

Once again why did you said 1 out of 3 ????

Quoted message said:

Quoted message said:

If the probability of a false positive is .01 then the probability of both
A and B samples receiving a false positives is .01 * .01 = .0001. I think

No I said 99% accuracy. Errors could be based on mishandling sample,
contamination, etc.... I just don't believe that a lab is 99.9%
accurate in their work.

Quoted message said:

We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120.

Yes

Quoted message said:

380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called cheaters)

So now you are applying 1% again.
Which means you are calculating based 0.1% accuracy. 1%x1%

Where we are diverging is you are applying 1% to the positives, while I
am applying 1%
to the total # of tests. IMHO, Accuracy of a test applies to the total
# of tests performed.

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

Quoted message said:

Quoted message said:

If the probability of a false positive is .01 then the probability of
both
A and B samples receiving a false positives is .01 * .01 = .0001. I
think

No I said 99% accuracy. Errors could be based on mishandling sample,
contamination, etc.... I just don't believe that a lab is 99.9%
accurate in their work.

Quoted message said:

We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120.

Yes

Quoted message said:

380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called
cheaters)

So now you are applying 1% again.
Which means you are calculating based 0.1% accuracy. 1%x1%

Of course no. The figure 380 of positive result is your, not the result of
some 1%

Quoted message said:

Where we are diverging is you are applying 1% to the positives, while I
am applying 1%
to the total # of tests. IMHO, Accuracy of a test applies to the total
# of tests performed.

CowPunk said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

I'm not a medical technician, and I don't play one on TV, but I have
heard from reliable sources that the false-positive and false-negative
rates in medical testing can be substantially different. For all I
know, this might be the rule rather than the exception.

An illustration with made-up numbers: Some test might have a false
positive rate of 10% (10% of those who are really "negative" are deemed
"positive" by the test) while only returning a 3% false negative rate
(only 3% of thoses truly "positive" are "missed" by the test). Again,
these numbers are entirely made up, only to illustrate the phenomenon.

Mark

<Montesquiou> wrote in
news:[email hidden]:

Quoted message said:

"Tim Lines" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

CowPunk said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came
back positive. These are just rough numbers off the top of
my head. It worked out to around 3.8% of all tests came back
positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad
testing.

It's been a million years since I took a probability class so
I must have just confused myself.

I too

Someone please straighten me out here.

Quoted message said:

If the probability of a false positive is .01 then the
probability of both A and B samples receiving a false
positives is .01 * .01 = .0001. I think that means that ~1.2
times a year someone innocent should fail both the A and B
sample despite being clean.

That's got to be wrong.

Let put it in other way.

We have 12000 test and 1% have a wrong result. 1% out of 12000 =
120.

In the 120 we have some Good Guys wrongly called cheaters, and
some cheaters called Good Guys

OK ?

Let see the distribution of this mistake :

380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys
called cheaters)

12000-380= 11620 Negative * 1 % = 116.2 (so 116.2 are cheaters
but found Good guys.)

Let see if there is a mistake . 116.2 + 3.8 = 120

Ok 120 is what we expected.

In short around 4 out 380 or 1 out 95 are poor guys called
cheaters but they are not..

On other side 116 out of 11620 or around 1 out of 100 are lucky
cheaters

Once again why did you said 1 out of 3 ????

Please see:

http://yudkowsky.net/bayes/bayes.html

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

1% of 12000 = 120

120:380 ~ 1:3

Oh my friend !!!

With all due respect if it is way they teach statistic in your country ...
You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Since your original post you DECIDED that 1% of the test were wrong.

So 1% of the 380 positive (that you DECIDED BY YOUR OWN) are wrong.

1% of 380 is 3.8.

Turn your problem the way you want 1% is allway 1% and NEVER 1:3 (33.33 %)
!!

Oh my God, pls help me !

The real numbers are:

1. If the test is proved correct, Floyd won't be back next year.
2. If the test is proved wrong, Floyd will have delayed his surgery due to
being distracted by the protesting such that he won't be back next year.

In either case the French win in keeping another USA contender out of the
race and improves their odd (hardly) of winning.

"CowPunk" <[email hidden]> wrote in message
news:[email hidden]...

Quoted message said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

Montesquiou said:

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

1% of 12000 = 120

120:380 ~ 1:3

Oh my friend !!!

With all due respect if it is way they teach statistic in your country ....
You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Since your original post you DECIDED that 1% of the test were wrong.

So 1% of the 380 positive (that you DECIDED BY YOUR OWN) are wrong.

1% of 380 is 3.8.

Turn your problem the way you want 1% is allway 1% and NEVER 1:3 (33.33 %)
!!

Oh my God, pls help me !

I am here and I will help you.

First, in each test there is an A and B sample and the test is done on
each. So if the there is a 1% chance of error on any give sample,
then the probablitity or an error both is found be multiplying .01
times .01 or .0001 or 0.01%.

Second, do not assume any error percentage until one appears in the
scientific literature, that is one that has been established with a
proven protocol and by actual perfroming many blind tests with samples
of known quality. One of the difficulties in this area is that test
error rates have not been established and made publicly available.

"Chris" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

The real numbers are:

1. If the test is proved correct, Floyd won't be back next year.
2. If the test is proved wrong, Floyd will have delayed his surgery due
to being distracted by the protesting such that he won't be back next
year.

In either case the French win in keeping another USA contender out of the
race and improves their odd (hardly) of winning.

Dear Chris,

Pls, There is no place here for political and/or Nationalist position.
I am very desapointed when I read such a post.
In the same way there is today in France the idea that the americans are
doing their best for to destroy the Tour de France.
It is a stupid idea.
Pls, I am sure that You and I want the same thing.
To eliminate the cheaters. You are doing it in with Gatlin and I
congratulate the US Lab. Unless you believe us, the French, are also behind
the scandal......

Quoted message said:

"CowPunk" <[email hidden]> wrote in message
news:[email hidden]...

Quoted message said:

Let's assume that the labs and their tests are 99% accurate.

The UCI did around 12000 tests last year, and about 380 came back
positive. These are just rough numbers off the top of my head.
It worked out to around 3.8% of all tests came back positive.

So, if you take that 99% accuracy number and apply it,
you end up with roughly 1 out of 3 positives due to bad testing.

Chris a écrit :

Quoted message said:

The real numbers are:

1. If the test is proved correct, Floyd won't be back next year.
2. If the test is proved wrong, Floyd will have delayed his surgery due to
being distracted by the protesting such that he won't be back next year.

In either case the French win in keeping another USA contender out of the
race and improves their odd (hardly) of winning.

Hooray, [censored].

<[email hidden]> a écrit dans le message de news:
[email hidden]...

Montesquiou said:

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

1% of 12000 = 120

120:380 ~ 1:3

Oh my friend !!!

With all due respect if it is way they teach statistic in your country
...
You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Since your original post you DECIDED that 1% of the test were wrong.

So 1% of the 380 positive (that you DECIDED BY YOUR OWN) are wrong.

1% of 380 is 3.8.

Turn your problem the way you want 1% is allway 1% and NEVER 1:3 (33.33 %)
!!

Oh my God, pls help me !

I am here and I will help you.

First, in each test there is an A and B sample and the test is done on
each. So if the there is a 1% chance of error on any give sample,
then the probablitity or an error both is found be multiplying .01
times .01 or .0001 or 0.01%.

Second, do not assume any error percentage until one appears in the
scientific literature, that is one that has been established with a
proven protocol and by actual perfroming many blind tests with samples
of known quality. One of the difficulties in this area is that test
error rates have not been established and made publicly available.

***

Correct.

It was so difficult for me to explain to him his wrong mathematical
reasoning that I did not even argued on the wrong initial suppositions he
did.

Montesquiou said:

Oh my friend !!!

With all due respect if it is way they teach statistic in your country ...
You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Why don't you just ask Kunich. He is the rbr expert on virtual probability
theory.

Quoted message said:

Montesquiou said:

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

1% of 12000 = 120

120:380 ~ 1:3

Oh my friend !!!

With all due respect if it is way they teach statistic in your country ...
You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Since your original post you DECIDED that 1% of the test were wrong.

So 1% of the 380 positive (that you DECIDED BY YOUR OWN) are wrong.

1% of 380 is 3.8.

Turn your problem the way you want 1% is allway 1% and NEVER 1:3 (33.33 %)
!!

Oh my God, pls help me !

I am here and I will help you.

First, in each test there is an A and B sample and the test is done on
each. So if the there is a 1% chance of error on any give sample,
then the probablitity or an error both is found be multiplying .01
times .01 or .0001 or 0.01%.

Second, do not assume any error percentage until one appears in the
scientific literature, that is one that has been established with a
proven protocol and by actual perfroming many blind tests with samples
of known quality. One of the difficulties in this area is that test
error rates have not been established and made publicly available.

Wouldn't that be part of promulgating a test? I would expect that before a test
is used it would be required that it's accuracy be measured. I'd also expect
that the development and proving of such a test be subject to peer review.

If that were not done I don't see how the testing protocol could be described as
"scientific" or be given the credence that laymen often accord to Science.

Ron

in message <[email hidden]>, Chris

(') said:

The real numbers are:

1. If the test is proved correct, Floyd won't be back next year.
2. If the test is proved wrong, Floyd will have delayed his surgery
due to being distracted by the protesting such that he won't be back
next year.

In either case the French win in keeping another USA contender out of
the race and improves their odd (hardly) of winning.

Petulant, xenophobic and paranoid.

--
[email hidden] (Simon Brooke) http://www.jasmine.org.uk/~simon/
Das Internet is nicht fuer gefingerclicken und giffengrabben... Ist
nicht fuer gewerken bei das dumpkopfen. Das mausklicken sichtseeren
keepen das bandwit-spewin hans in das pockets muss; relaxen und
watchen das cursorblinken. -- quoted from the jargon file

<Montesquiou> wrote in news:[email hidden]:

Quoted message said:

"CowPunk" <[email hidden]> a écrit dans le message de news:
[email hidden]...

Quoted message said:

1% of 12000 = 120

120:380 ~ 1:3

Oh my friend !!!

With all due respect if it is way they teach statistic in your
country ... You are lost.

However as I have many friends in the USA and I know they are not so
ignorants in Math, I believe the problem is your.

Since your original post you DECIDED that 1% of the test were wrong.

So 1% of the 380 positive (that you DECIDED BY YOUR OWN) are wrong.

1% of 380 is 3.8.

Turn your problem the way you want 1% is allway 1% and NEVER 1:3
(33.33 %) !!

Oh my God, pls help me !

I believe what the original poster meant was that the occurance of a false
positive is 1%. That would mean that after taking 12000 tests, 1% or 120
false positive results would be expected. If there were a total of 380
positives out of that same 12000, 120 would be false ones and the other 260
would be real.

HTH

Ed

CowPunk said:

Quoted message said:

Quoted message said:

If the probability of a false positive is .01 then the probability of both
A and B samples receiving a false positives is .01 * .01 = .0001. I think

No I said 99% accuracy. Errors could be based on mishandling sample,
contamination, etc.... I just don't believe that a lab is 99.9%
accurate in their work.

Quoted message said:

We have 12000 test and 1% have a wrong result. 1% out of 12000 = 120.

Yes

Quoted message said:

380 Positive * 1% = 3.8 ( so 3.8 out of 380 are clean guys called cheaters)

So now you are applying 1% again.
Which means you are calculating based 0.1% accuracy. 1%x1%

Where we are diverging is you are applying 1% to the positives, while I
am applying 1%
to the total # of tests. IMHO, Accuracy of a test applies to the total
# of tests performed.

Dear Bovine:

Your reasoning is wrong. the french philosopher's explanation was on
target. 1% error does no mean that 1 out of 3 tests will be wrong. No
matter how you articulate the problem.

Andres