"Torsten Brinch" wrote in message
...
On Sat, 30 Aug 2003 02:01:27 GMT, "Gordon Couger"
wrote:


"Torsten Brinch" wrote in message
.. .
On Fri, 29 Aug 2003 21:42:10 GMT, "Gordon Couger"
wrote:

All the data points used in the study except the discarded
ones are in the paper. That should be enough to duplicate
the statistics.

Um. The plotted points you see in the graphics, and the numbers in
tables are not raw values, they are means (n=3), three replicates
per treatment and sampling date.

However, apparently it is your hypothesis while looking at these data
points, that they are not all there, that some data points have been
discarded. But hey, that should be easily verifiable. Check, and you
should find for some sampling dates in plots and tables, that data
points are missing. :-)

Unfortunately for your hypothesis, they are all there.

There is no hypothesis the paper clearly states outliers are discarded.

I am talking about your hypothesis that "data that didn't agree with
the findings was discarded".

God help you, if you have nothing else to base this on, than what is
clearly stated in the paper, that outliers in raw data were removed
from datasets before variance homogenity of data was evaluated
in residual plots. Whatever you may think of removal of outliers at
this particular stage in the statistical analysis, it obviously does
not and cannot constitute the authors discarding of data that doesn't
agree with the findings. There are no findings at this stage, just a
mass of raw values, unfitted to any model, untested for any
significant differences between them.


You have no idea when the data was discarded. It may have been when it was
found to be inconvenient in the statistical calculations. The results had
been quoted many times before any work was done. Who knows in what order the
work were done.

Gordon

"Torsten Brinch" wrote in message
...
On Sat, 30 Aug 2003 02:01:27 GMT, "Gordon Couger"
wrote:


"Torsten Brinch" wrote in message
.. .
On Fri, 29 Aug 2003 21:42:10 GMT, "Gordon Couger"
wrote:

All the data points used in the study except the discarded
ones are in the paper. That should be enough to duplicate
the statistics.

Um. The plotted points you see in the graphics, and the numbers in
tables are not raw values, they are means (n=3), three replicates
per treatment and sampling date.

However, apparently it is your hypothesis while looking at these data
points, that they are not all there, that some data points have been
discarded. But hey, that should be easily verifiable. Check, and you
should find for some sampling dates in plots and tables, that data
points are missing. :-)

Unfortunately for your hypothesis, they are all there.

There is no hypothesis the paper clearly states outliers are discarded.

I am talking about your hypothesis that "data that didn't agree with
the findings was discarded".

God help you, if you have nothing else to base this on, than what is
clearly stated in the paper, that outliers in raw data were removed
from datasets before variance homogenity of data was evaluated
in residual plots. Whatever you may think of removal of outliers at
this particular stage in the statistical analysis, it obviously does
not and cannot constitute the authors discarding of data that doesn't
agree with the findings. There are no findings at this stage, just a
mass of raw values, unfitted to any model, untested for any
significant differences between them.


You have no idea when the data was discarded. It may have been when it was
found to be inconvenient in the statistical calculations. The results had
been quoted many times before any work was done. Who knows in what order the
work were done.

Gordon

On Sun, 31 Aug 2003 09:00:17 GMT, "Gordon Couger"
wrote:


"Torsten Brinch" wrote in message
.. .
On Sat, 30 Aug 2003 02:01:27 GMT, "Gordon Couger"
wrote:

There is no hypothesis the paper clearly states outliers are discarded.

I am talking about your hypothesis that "data that didn't agree with
the findings was discarded".

God help you, if you have nothing else to base this on, than what is
clearly stated in the paper, that outliers in raw data were removed
from datasets before variance homogenity of data was evaluated
in residual plots. Whatever you may think of removal of outliers at
this particular stage in the statistical analysis, it obviously does
not and cannot constitute the authors discarding of data that doesn't
agree with the findings. There are no findings at this stage, just a
mass of raw values, unfitted to any model, untested for any
significant differences between them.


You have no idea when the data was discarded.

Come, the statistical analysis section in the paper clearly
describes the series of steps taken in the analysis, in the
order they were taken. How can anyone read that section with
comprehension and escape with no idea when outliers were
removed from raw data?

It may have been when it was found to be inconvenient in
the statistical calculations. snip

And, what is the relation, if any, between this hypothesis,
and your original hypothesis that "data that didn't
agree with the findings was discarded"?

I mean, are you just re-expressing that original hypothesis
in a fuzzy low-key manner, or are you referring to some
inconvenience of having a gross outlier in a residual plot?

"Torsten Brinch" wrote in message
...
On Sun, 31 Aug 2003 09:00:17 GMT, "Gordon Couger"
wrote:


"Torsten Brinch" wrote in message
.. .
On Sat, 30 Aug 2003 02:01:27 GMT, "Gordon Couger"
wrote:

There is no hypothesis the paper clearly states outliers are
discarded.

I am talking about your hypothesis that "data that didn't agree with
the findings was discarded".

God help you, if you have nothing else to base this on, than what is
clearly stated in the paper, that outliers in raw data were removed
from datasets before variance homogenity of data was evaluated
in residual plots. Whatever you may think of removal of outliers at
this particular stage in the statistical analysis, it obviously does
not and cannot constitute the authors discarding of data that doesn't
agree with the findings. There are no findings at this stage, just a
mass of raw values, unfitted to any model, untested for any
significant differences between them.


You have no idea when the data was discarded.

Come, the statistical analysis section in the paper clearly
describes the series of steps taken in the analysis, in the
order they were taken. How can anyone read that section with
comprehension and escape with no idea when outliers were
removed from raw data?

It may have been when it was found to be inconvenient in
the statistical calculations. snip

And, what is the relation, if any, between this hypothesis,
and your original hypothesis that "data that didn't
agree with the findings was discarded"?

I mean, are you just re-expressing that original hypothesis
in a fuzzy low-key manner, or are you referring to some
inconvenience of having a gross outlier in a residual plot?

When the findings are used in a fraudulent manner before the work that the
paper is written from is preformed am strongly suspicious of the paper and
all connect to it. When the statistical claims they make don't agree with
the data they publish I am more cynical about it.

I went to the effort ot have experts look at the paper and they came to the
same conclusions and you can call OSU like I did and find out what Ms.
Ingram's relationship with them was when she claimed affiliation with them
when she had none. Or you can look up her CV and it verifies her employment
dates at OSU and shows she was not working for them and only had courtesy
privileges. You can get courtesy privileges at any land grant university by
just asking.

If you can make that pig of a paper sing by making the statistics work I
will continue the discussion.

Gordon

On Sun, 31 Aug 2003 20:45:07 GMT, "Gordon Couger"
wrote:

When the findings are used in a fraudulent manner before the work that the
paper is written from is preformed am strongly suspicious of the paper and
all connect to it.

But, that's highly circumstantial, is it not, if you want to prove
abominable discarding of data? :-)

However, let's see your evidence for the claim that findings of
the paper were used, before the work that the paper is written
from was performed.

When the statistical claims they make don't agree with
the data they publish I am more cynical about it.

You must be more specific, or noone will know what you are
critisising. Which statistical claims don't agree with
which data? If you can't answer that, you do not
have a critique of substance worth relating to.

I went to the effort ot have experts look at the paper and they came to the
same conclusions

Understand that unidentified experts making unidentified conclusions
that happens to agree with whatever you say just doesn't cut it.

and you can call OSU like I did and find out what Ms.
Ingram's relationship with them snip

Ms. Inghams affiliation is irrelevant to the question, if data
points was discarded that did not agree with the findings in
that paper.

If you can make that pig of a paper sing by making the statistics work
I will continue the discussion.

That's very kind of you. However don't you think it is about time
you coughed up some evidence for your claim that data that did not
agree with findings was discarded? How many times have you been
asked for that now. Five, seven times?

"Torsten Brinch" wrote in message
...
On Sun, 31 Aug 2003 20:45:07 GMT, "Gordon Couger"
wrote:

When the findings are used in a fraudulent manner before the work that
the
paper is written from is preformed am strongly suspicious of the paper
and
all connect to it.

But, that's highly circumstantial, is it not, if you want to prove
abominable discarding of data? :-)

However, let's see your evidence for the claim that findings of
the paper were used, before the work that the paper is written
from was performed.

When the statistical claims they make don't agree with
the data they publish I am more cynical about it.

You must be more specific, or noone will know what you are
critisising. Which statistical claims don't agree with
which data? If you can't answer that, you do not
have a critique of substance worth relating to.

I went to the effort ot have experts look at the paper and they came to
the
same conclusions

Understand that unidentified experts making unidentified conclusions
that happens to agree with whatever you say just doesn't cut it.

and you can call OSU like I did and find out what Ms.
Ingram's relationship with them snip

Ms. Inghams affiliation is irrelevant to the question, if data
points was discarded that did not agree with the findings in
that paper.

If you can make that pig of a paper sing by making the statistics work
I will continue the discussion.

That's very kind of you. However don't you think it is about time
you coughed up some evidence for your claim that data that did not
agree with findings was discarded? How many times have you been
asked for that now. Five, seven times?

I have no idea what the effect of the discarded data did to the study did.
That's the point.

Gordon

On Mon, 01 Sep 2003 03:30:41 GMT, "Gordon Couger"
wrote:

I have no idea what the effect of the discarded data did to the study did.
That's the point.

Well, ask yourself what happens to a data set if you add an outlier
to it: The overall variance in data increases, and the ANOVA may
come out with either too low F-values to allow you to calculate a
figure for least significant difference (LSD) or you may end up with
an inflated figure for LSD. The effect of this may be that you will
reject otherwise significant differences.

Now turn that around: Without the outlier, finding significant
differences will be more likely. In other words, you can reasonably
suspect that those significant difference that are found without the
outlier, might turn into insignificant differences with the outlier
added back in.

However, this study actually found very few significant differences,
although some of those that were found were striking.

The main findings, in short, was that plants in the GE bacteria setup
turned chlorotic and wilted, apparently concomitant with a flush
period of nematode growth during which nematodes reached higher
numbers, and, with fungal feeding nematodes dominating.
Compared to this plants in the non-GE bacteria setups,plants grew
well, nematode numbers did not flush just as much, and bacterial
feeding nematodes remained dominating.

And here's the crunch, there is no way any discarding of outliers can
have 'produced' the finding that plants in the GE bacteria setup
wilted and died, while the plants in the nonGE bacteria setup grew.

So, from a cool minded perspective, you may well have concerns
as a matter of principle as regards handling of outliers,
but you cannot have concerns that this handling has affected
the main findings of the study.

Quite generally, a criticism of a study, on counts that do not
affect its main findings, is insubstantial. Perhaps some would
call such criticism mere nitpicking, I would go that far, because
it may well be educating. However, the point is, you can't 'kill'
a study by flawing it of something that does not change its
conclusions.

On Mon, 01 Sep 2003 03:30:41 GMT, "Gordon Couger"
wrote:

"Torsten Brinch" wrote in message
.. .
On Sun, 31 Aug 2003 20:45:07 GMT, "Gordon Couger"
wrote:

When the findings are used in a fraudulent manner before
the work that the paper is written from is preformed am
strongly suspicious of the paper and all connect to it.

..let's see your evidence for the claim that findings of
the paper were used, before the work that the paper is written
from was performed.

Let's see your evidence, Gordon.

In sci.agriculture Gordon Couger wrote:

"Torsten Brinch" wrote in message
...

On Sun, 31 Aug 2003 20:45:07 GMT, "Gordon Couger"
wrote:

If you can make that pig of a paper sing by making the statistics work
I will continue the discussion.

That's very kind of you. However don't you think it is about time
you coughed up some evidence for your claim that data that did not
agree with findings was discarded? How many times have you been
asked for that now. Five, seven times?

I have no idea what the effect of the discarded data did to the study did.
That's the point.

No you need to be quite good at the subject to deal properly with
outliers. I am thinking that sometimes people do not eliminate them when
they should be, and others do but don't acknowledge it.

Imagine you represent a conservative govt applying as little as possible
health funding to a village of 100 people of mainly low income, based on
whether they can pay for it themselves or not. When calculating the
average will you include the income of the one multi-millionaire in the
village? That would make the average income rather higher, so you can fund
less. But the other 99 people would have no ability to pay, consequently.
The place would become a real eyesore.

Then if you were looking for how much the village could potentially donate
to a cause would the high earner still be an outlier?

If you were recording times for a cross country race would you include
ones where runners had obviously taken a short cut or joined the race some
way through it because they were rather briefer than really possible?
(Memories of school cross-countries). Well you might if you were trying to
catch cheats, or runners mistaken about the route.

On 1 Sep 2003 11:52:50 GMT, Brian Sandle
wrote:

In sci.agriculture Gordon Couger wrote:

"Torsten Brinch" wrote in message
...

On Sun, 31 Aug 2003 20:45:07 GMT, "Gordon Couger"
wrote:

If you can make that pig of a paper sing by making the statistics work
I will continue the discussion.

That's very kind of you. However don't you think it is about time
you coughed up some evidence for your claim that data that did not
agree with findings was discarded? How many times have you been
asked for that now. Five, seven times?

I have no idea what the effect of the discarded data did to the study did.
That's the point.

No you need to be quite good at the subject to deal properly with
outliers. I am thinking that sometimes people do not eliminate them when
they should be, and others do but don't acknowledge it.

Imagine you represent a conservative govt applying as little as possible
health funding to a village of 100 people of mainly low income, based on
whether they can pay for it themselves or not. When calculating the
average will you include the income of the one multi-millionaire in the
village? That would make the average income rather higher, so you can fund
less. But the other 99 people would have no ability to pay, consequently.
The place would become a real eyesore.

But that's not an outlier problem, Brian. Indeed, there's not much of
a statistical problem in it :-) You have sampled the whole population,
you know the income of each and every individual in it, you know their
average income. The average is the average. It is just not a very good
descriptor for what the politicians want to describe.

Then if you were looking for how much the village could potentially donate
to a cause would the high earner still be an outlier?

Yes. In that situation average income might be a more suitable
descriptor. But again, this is not an outlier problem. The high earner
is known to be part of the population studied, so the data point
representing his income can never be considered an outlier.

If you were recording times for a cross country race would you include
ones where runners had obviously taken a short cut or joined the race some
way through it because they were rather briefer than really possible?
(Memories of school cross-countries). Well you might if you were trying to
catch cheats, or runners mistaken about the route.

That's more like it. If you observe from the recorded racing times
that all racers completed the race within a time range of 3-7 hours,
except one racer -- who has been recorded as completing it in 7
minutes -- that does raise the question if the racing time of this
runner should not be consider an outlier.

In sci.agriculture Torsten Brinch wrote:
On 1 Sep 2003 11:52:50 GMT, Brian Sandle
wrote:
No you need to be quite good at the subject to deal properly with
outliers. I am thinking that sometimes people do not eliminate them when
they should be, and others do but don't acknowledge it.

Imagine you represent a conservative govt applying as little as possible
health funding to a village of 100 people of mainly low income, based on
whether they can pay for it themselves or not. When calculating the
average will you include the income of the one multi-millionaire in the
village? That would make the average income rather higher, so you can fund
less. But the other 99 people would have no ability to pay, consequently.
The place would become a real eyesore.

But that's not an outlier problem, Brian. Indeed, there's not much of
a statistical problem in it :-)

Except in that part of statistics is deciding what measures to use and
what to measure.

You have sampled the whole population,
you know the income of each and every individual in it, you know their
average income. The average is the average. It is just not a very good
descriptor for what the politicians want to describe.

Then you might change to the middle income. That might not work either if
there is a big tail of very low incomes.

Then if you were looking for how much the village could potentially donate
to a cause would the high earner still be an outlier?

Yes. In that situation average income might be a more suitable
descriptor. But again, this is not an outlier problem. The high earner
is known to be part of the population studied, so the data point
representing his income can never be considered an outlier.

So you might change from a purely latitude and longitude basis for the
sample to some other. Perhaps it is the subset of employees in the region.

Say you wanted to persuade people that potatoes in general are not high on
solanine. How many sweet potatoes are you allowed in the sample?

Given the figures the sweet potatoes might appear as outliers. This might
lead back to calling into question whether a sweet potato is a potato.

I think Gordon has a little point, that he needed to be told a bit more
about the outlier categorizing. But when you search the web for how
frequently `Monsanto' occurs in studies mentioning outliers, how much do
you get?

If you were recording times for a cross country race would you include
ones where runners had obviously taken a short cut or joined the race some
way through it because they were rather briefer than really possible?
(Memories of school cross-countries). Well you might if you were trying to
catch cheats, or runners mistaken about the route.

That's more like it. If you observe from the recorded racing times
that all racers completed the race within a time range of 3-7 hours,
except one racer -- who has been recorded as completing it in 7
minutes -- that does raise the question if the racing time of this
runner should not be consider an outlier.

I think they used to be picked up in a car and dropped off again near the
finish, to come in not suspiciously too early. In this case the finishing
time is not an `adequate measure' of running ability.

On 28 Aug 2003 23:29:25 GMT, Brian Sandle
posted:

In sci.agriculture Mooshie peas wrote:
On 28 Aug 2003 14:23:53 GMT, Brian Sandle
posted:
Do you have the database of withdrawn applications and why they were
withdrawn?

A while back I referred to a submission by Jack Heineman against
approval of another organisation's application for GM work in NZ.

The other organisation withdrew the application.

Was Dr Ingham's work, connected with the EPA, the cause of a
dangerous or dubious application being withdrawn?

No idea. You'll have to eyeball your regulator's documentation, I
would think.

That's right, where do they keep the records of withdrawn applications and
what has caused the withdrawal?

I suspect a bash at Google would find some information about this if
it was at all interesting. If you don't find anything, it's likely
that there was nothing newsworthy about it. Everything vaguely
newsworth is published on the Web, IME

On 28 Aug 2003 23:31:26 GMT, Brian Sandle
posted:

In sci.agriculture Mooshie peas wrote:
On 28 Aug 2003 13:51:31 GMT, Brian Sandle
posted:

In sci.med.nutrition Mooshie peas wrote:

=======================================
Evidence in Rebuttal - Life Sciences Network
20 February 2001, 9:24 am
Press Release: New Zealand Life Sciences Network

Conclusion:

In conclusion, it is our opinion that Dr Ingham has presented
inaccurate, careless and exaggerated information to the Royal
Commission; incorrectly interpreting published scientific information
and generating speculative doomsday scenarios that are not
scientifically supportable.

As they want others to be exact so they must be taken at their word.

But they leave plenty of room for misunderstanding:

Read the rest of it.

I did.

In conclusion, it is our opinion that Dr Ingham has presented
inaccurate, careless and exaggerated information

That could either mean that all the info is classifed that way, or
rather that there has been some innacuracy, some lack of care. and
futhermore the interesting admission by Life Sciences Network -
exaggeration by Ingham's submission. So they are admitting some
truth to it just exaggerated.

Grasping at straws?
There is a tiny bit of truth in everything, that's life.

to the Royal
Commission; incorrectly interpreting published scientific information
and generating speculative doomsday scenarios that are not
scientifically supportable.

And the tone of that is that it is unlikely to kill off all the
plant life on the planet, therefore go ahead with it.

Nope. If some of it's wrong, then it must all be looked at
sceptically. And that's what happens.

Which is how to look at GM. But more than scepticism, rather fear.

But you have shown nothing wrong. All the lies that I've seen are on
the other side -- the anti-GM lobby. That UK "soil association" would
have to be the biggest liars and twisters since the silly "Biodynamic"
mob hit the airwaves here a while ago. My wireless set almost had
bodily harm done to it when the lies from that Holden spokeman for the
Soil Association were being spouted in an interview I recently heard.

On 1 Sep 2003 23:50:28 GMT, Brian Sandle
wrote:

In sci.agriculture Torsten Brinch wrote:
On 1 Sep 2003 11:52:50 GMT, Brian Sandle
wrote:
No you need to be quite good at the subject to deal properly with
outliers. I am thinking that sometimes people do not eliminate them when
they should be, and others do but don't acknowledge it.

Imagine you represent a conservative govt applying as little as possible
health funding to a village of 100 people of mainly low income, based on
whether they can pay for it themselves or not. When calculating the
average will you include the income of the one multi-millionaire in the
village? That would make the average income rather higher, so you can fund
less. But the other 99 people would have no ability to pay, consequently.
The place would become a real eyesore.

But that's not an outlier problem, Brian. Indeed, there's not much of
a statistical problem in it :-)

Except in that part of statistics is deciding what measures to use and
what to measure.

I must admit I find it the bigger problem in imagining myself as a
conservative govt representative.

You have sampled the whole population,
you know the income of each and every individual in it, you know their
average income. The average is the average. It is just not a very good
descriptor for what the politicians want to describe.

Then you might change to the middle income. That might not work either if
there is a big tail of very low incomes.

Perhaps, could we use some statistics on misuse of the public health
care system? You know, some people go to the doctor for no good reason
again and again, etc, we could get some numbers on that. We could say:

This wouldn't be a problem in a user-financed system. And, "the money
lies best in the citizen's pocket". There must be a basic health
system, but need it cost that much? We could talk: about healthy
competition in the health care industry, people choosing freely
between products on a free market for a wealth of health services.
Everything would become cheaper then, our society would be richer,
and we could all get better health care than we get it now for less
money, or a faster car. Clearly -no one- benefits from an inefficient
tax-funded health system -- [this is where we show a colorful graph of
our statistic on-screen] -- a public health sector, which is so
vulnerable to overuse and misappropriation of resources.

Then if you were looking for how much the village could potentially donate
to a cause would the high earner still be an outlier?

Yes. In that situation average income might be a more suitable
descriptor. But again, this is not an outlier problem. The high earner
is known to be part of the population studied, so the data point
representing his income can never be considered an outlier.

So you might change from a purely latitude and longitude basis for the
sample to some other. Perhaps it is the subset of employees in the region.

Say you wanted to persuade people that potatoes in general are not high on
solanine. How many sweet potatoes are you allowed in the sample?
Given the figures the sweet potatoes might appear as outliers. This might
lead back to calling into question whether a sweet potato is a potato.

I think we -must- have decided on that question way before looking for
outliers in our data. :-)

I think Gordon has a little point, that he needed to be told a bit more
about the outlier categorizing.

But, what if all he wants to know is that Ms.Ingham is a witch
and should be burned?

But when you search the web for how
frequently `Monsanto' occurs in studies mentioning outliers, how much do
you get?

Interesting problem. How do you best restrict a web search, to make it
return only studies?

Heh. Over at sci.ag. Gordon has posted this Monsanto funded study,
which, :-) hold on to your chair, apparently has discarded outliers,
without mentioning that it has done it , far less telling when or why.

You should see Gordon, he is really -rough- now on that poor Monsanto
report now because of that :-)

Just kidding. Gordon has studiously not said a word about it.

In sci.agriculture Mooshie peas wrote:
On 28 Aug 2003 23:29:25 GMT, Brian Sandle
posted:

In sci.agriculture Mooshie peas wrote:
On 28 Aug 2003 14:23:53 GMT, Brian Sandle
posted:
Do you have the database of withdrawn applications and why they were
withdrawn?

A while back I referred to a submission by Jack Heineman against
approval of another organisation's application for GM work in NZ.

The other organisation withdrew the application.

Was Dr Ingham's work, connected with the EPA, the cause of a
dangerous or dubious application being withdrawn?

No idea. You'll have to eyeball your regulator's documentation, I
would think.

That's right, where do they keep the records of withdrawn applications and
what has caused the withdrawal?

I suspect a bash at Google would find some information about this if
it was at all interesting. If you don't find anything, it's likely
that there was nothing newsworthy about it. Everything vaguely
newsworth is published on the Web, IME

I have checked again and
http://www.aphis.usda.gov/bbep/
is still not even giving out data for ordinary applications any
more.

Somehow I think the data about withdrawn applications in USA will be
kept covered. Then we won't be able to verify the claim about the
withdrawal of the GM Klebsiella application following Ingham's
research findings.