A * Titans * fight II: Jack Tomsky versus R. A.
Fisher
One said Null Hypotheses cannot be proved true or
even be accepted, the other, J. Tomsky states that
this is FULL WRONG.
**************
From the former thread’s discussion any new advance:
THE IMBECILE DUO (Jack Tomsky, John Smith) are not
available (and presented any valid argument) to fight
the well established by all Statisticians Fisher
principle that one cannot accept H0 as true: one
simply fail to reject the alternative hypotheses H1
(if the test do not falls in the to reject region).

The reader should remember that Luis Amaral Afonso writes incorrect null and alternative hypotheses, and does not admit that they are incorrect.  Clearly he believes them to be correct.  For example, when writing the null and alternative hypotheses, put the equal sign with the alternative hypothesis, not with the null.  For example, Luis Amaral Afonso writes:
H0: pX - pY > 0 , Ha: pX < = pY.

THREAD:  CLOPPER-PEARSON intervals to compare Binomial Proportions  THREAD STARTED:  Jul 23 2007, 5:23 pm

A * Titans * fight II:  Jack Tomsky versus R. A.
Fisher
One said Null Hypotheses cannot be proved true or
even be accepted, the other, J. Tomsky states that
this is FULL WRONG.
**************
From the former thread’s discussion any new advance:
THE IMBECILE DUO (Jack Tomsky, John Smith) are not
available (and presented any valid argument) to fight
the well established by all Statisticians Fisher
principle that one cannot accept H0 as true: one
simply fail to reject the alternative hypotheses H1
(if the test do not falls in the to reject region).

Fisher never accepted or rejected the alternative hypothesis.  That's because under Fisher's framework, he did not have an alternative hypothesis.  Once the alternative hypothesis H1 is specified, it is outside the Fisher framework and into the Neyman-Pearson framework, where the null hypothesis can be either accepted or rejected.

Jack (moderator)

On 7 Jul, 18:54, Jack Tomsky <jtom... at (no spam) ix.netcom.com
wrote:
A * Titans * fight II: Jack Tomsky versus R. A.
Fisher
One said Null Hypotheses cannot be proved true or
even be accepted, the other, J. Tomsky states
that
this is FULL WRONG.
**************
From the former thread’s discussion any new
advance:
THE IMBECILE DUO (Jack Tomsky, John Smith) are
not
available (and presented any valid argument) to
fight
the well established by all Statisticians Fisher
principle that one cannot accept H0 as true: one
simply fail to reject the alternative hypotheses
H1
(if the test do not falls in the to reject
region).

Fisher never accepted or rejected the alternative
hypothesis. That's because under Fisher's framework,
he did not have an alternative hypothesis. Once the
alternative hypothesis H1 is specified, it is outside
the Fisher framework and into the Neyman-Pearson
framework, where the null hypothesis can be either
accepted or rejected.

Jack (moderator)

NO : Neyman - Pearson never ACCEPTS THE NULL
HYPOTHESES.
STATISTICIANS ARE UNANIMOUSLY CONCERNED:
Luis Amaral Afonso [the moderator DESTROYER]

To the stage they turned up, unexpectedly, two odd
players: the type I and the type II errors.
For the tricky, indecent and unethical Jack Tomsky
even the use of a clear * petition principle *
doesn’t stop him: THE ERRORS EXISTENCE ARE DEFINED IF
H0 is true (Type I), or Ha is true (Type II). THEY
ARE WORTHLESS TO THE DISCUSSION WE ARE HAVING HERE.
It is a clear circular thought (However, clearly,
they are very useful to keep the confusion alive; the
Jacks goal to try to erase his BRUNDLE).

Luis Amaral Afonso [The moderator destroyer]
.

*********
Two sample parameters comparison (observed p1^ and
p2^)___H0: p1 = p2 ___H1: p1=/ p2.

Let be W (data| H0) a complete, unbiased, known
statistics, of the continuous parameter p. The
confidence interval for the difference D= |p1-p2| is

__|p1^ - p2^| - W(1-a) <= D <= |p1^ - p2^| - W(a)

when the difference |p1-p2| of the two parameters. By
a= alpha is noted the significance level.

Because the above C. I. doesn’t degenerate in a
single point, consequently W(a) = W(1-a), ONE CAN
NEVER BE SURE OF THE TWO PARAMETER´S EQUALITY, say
D=0.
Furthermore, for significative tests, the absolute
difference on parameters (at alpha significance
level) must be greater than D.

Luis Amaral Afonso [The moderator

On 7 Jul, 18:54, Jack Tomsky <jtom... at (no spam) ix.netcom.com
wrote:
A * Titans * fight II:  Jack Tomsky versus R. A.
Fisher
One said Null Hypotheses cannot be proved true or
even be accepted, the other, J. Tomsky states
that
this is FULL WRONG.
**************
From the former thread’s discussion any new
advance:
THE IMBECILE DUO (Jack Tomsky, John Smith) are
not
available (and presented any valid argument) to
fight
the well established by all Statisticians Fisher
principle that one cannot accept H0 as true: one
simply fail to reject the alternative hypotheses
H1
(if the test do not falls in the to reject
region).

Fisher never accepted or rejected the alternative
hypothesis.  That's because under Fisher's framework,
he did not have an alternative hypothesis.  Once the
alternative hypothesis H1 is specified, it is outside
the Fisher framework and into the Neyman-Pearson
framework, where the null hypothesis can be either
accepted or rejected.

Jack (moderator)

NO : Neyman - Pearson never ACCEPTS THE NULL
HYPOTHESES.
STATISTICIANS ARE UNANIMOUSLY CONCERNED:
Luis Amaral Afonso [the moderator DESTROYER]

There are quite a few statisticians who believe it is permissible to accept the null hypothesis.  Here are a few besides Neyman and Pearson.

http://www.statistics.com/resources/glossary/t/type2err.php

Type II error

In a test of significance, Type II error is the error of accepting the null hypothesis when it is false -- of failing to declare a real difference as statistically significant. Obviously, the bigger your samples, the more likely your test is to detect any difference that exists. The probability of detecting a real difference of specified size (i.e. of not committing a Type II error) is called the power of the test.

http://mathworld.wolfram.com/TypeIIError.html

Type II error

An error in a statistical test which occurs when a false hypothesis is accepted (a false positive in terms of the null hypothesis).

http://www.investopedia.com/terms/t/type-II-error.asp

A type II error accepts the null hypothesis, although the alternative hypothesis is the true state of nature.

http://www.merriam-webster.com/dictionary/type%20ii%20error

Main Entry:
type II error

acceptance of the null hypothesis in statistical testing when it is false..

http://dictionary.reference.com/browse/type%20ii%20error

Type II error
noun
the error made in the statistical testing of a hypothesis by accepting the null hypothesis when it is actually false.

http://www.everythingbio.com/glos/definition.php?word=type+II+error

Definition of type II error:
In statistics the accepting of a false hypothesis.

http://www.childrensmercy.org/stats/definitions/typeii.htm

A Type II error is accepting the null hypothesis when the null hypothesis is false.

http://www.statsdirect.com/help/basics/pval.htm

Type I error is the false rejection of the null hypothesis and type II error is the false acceptance of the null hypothesis.

http://wiki.answers.com/Q/When_performing_a_study_what_is_a_type_2_error

A type 2 error is when you accept your null hypothesis when in fact the alternative is true. A type 2 error is also known as a false negative.

Jack (moderator)- Hide quoted text -

- Show quoted text -

I said:

*** Two sample parameters comparison (observed p1^
and p2^)___H0: p1 = p2 ___H1: p1=/ p2.

Let be W (data| H0) a complete, unbiased, known
n statistics, of the continuous parameter p. The
confidence interval for the difference D= |p1-p2| is

__|p1^ - p2^| - W(1-a) <= D <= |p1^ - p2^| - W(a)

when the difference |p1-p2| of the two parameters.
. By a= alpha is noted the significance level. ***


My response

THE IMBECILE DUO states that if
____________p1^ = p2^__________[1]
D=0 and the Null Hypotheses is proved TRUE!!!!!!!
********************************

NOTE THAT I HAD THE CONCERN TO SAY
__________p is a continuous parameter
__and everyone sufficiently intelligent get
immediately that the proviso must have some
justification. What? Is it to prevent tat [1] never
occurs!!!!
Think at the random samples X~N(0,1):10,
Y~N(0.1):10___p1^ = Xhat , p2^ = Yhat : find the
probability to observe [1] !!!

Once more the IMBECILE DUO had shown I found the
designative exact term! Congratulate me!

Luis Amaral Afonso [The moderator destroyer]

On Jul 7, 8:50 pm, Jack Tomsky
jtom... at (no spam) ix.netcom.com> wrote:
On 7 Jul, 18:54, Jack Tomsky
jtom... at (no spam) ix.netcom.com
wrote:
A * Titans * fight II: Jack Tomsky versus R.
A.
Fisher
One said Null Hypotheses cannot be proved
true or
even be accepted, the other, J. Tomsky states
that
this is FULL WRONG.
**************
From the former thread’s discussion any new
advance:
THE IMBECILE DUO (Jack Tomsky, John Smith)
are
not
available (and presented any valid argument)
to
fight
the well established by all Statisticians
Fisher
principle that one cannot accept H0 as true:
one
simply fail to reject the alternative
hypotheses
H1
(if the test do not falls in the to reject
region).

Fisher never accepted or rejected the
alternative
hypothesis. That's because under Fisher's
framework,
he did not have an alternative hypothesis. Once
the
alternative hypothesis H1 is specified, it is
outside
the Fisher framework and into the Neyman-Pearson
framework, where the null hypothesis can be
either
accepted or rejected.

Jack (moderator)

NO : Neyman - Pearson never ACCEPTS THE NULL
HYPOTHESES.
STATISTICIANS ARE UNANIMOUSLY CONCERNED:
Luis Amaral Afonso [the moderator DESTROYER]

There are quite a few statisticians who believe it
is permissible to accept the null hypothesis. Here
are a few besides Neyman and Pearson.


http://www.statistics.com/resources/glossary/t/type2er
r.php

Type II error

In a test of significance, Type II error is the
error of accepting the null hypothesis when it is
false -- of failing to declare a real difference as
statistically significant. Obviously, the bigger your
samples, the more likely your test is to detect any
difference that exists. The probability of detecting
a real difference of specified size (i.e. of not
committing a Type II error) is called the power of
the test.

http://mathworld.wolfram.com/TypeIIError.html

Type II error

An error in a statistical test which occurs when a
false hypothesis is accepted (a false positive in
terms of the null hypothesis).


http://www.investopedia.com/terms/t/type-II-error.asp

A type II error accepts the null hypothesis,
although the alternative hypothesis is the true state
of nature.


http://www.merriam-webster.com/dictionary/type%20ii%20
error

Main Entry:
type II error

acceptance of the null hypothesis in statistical
testing when it is false.


http://dictionary.reference.com/browse/type%20ii%20err
or

Type II error
noun
the error made in the statistical testing of a
hypothesis by accepting the null hypothesis when it
is actually false.


http://www.everythingbio.com/glos/definition.php?word=
type+II+error

Definition of type II error:
In statistics the accepting of a false hypothesis.


http://www.childrensmercy.org/stats/definitions/typeii
.htm

A Type II error is accepting the null hypothesis
when the null hypothesis is false.

http://www.statsdirect.com/help/basics/pval.htm

Type I error is the false rejection of the null
hypothesis and type II error is the false acceptance
of the null hypothesis.


http://wiki.answers.com/Q/When_performing_a_study_what
_is_a_type_2_error

A type 2 error is when you accept your null
hypothesis when in fact the alternative is true. A
type 2 error is also known as a false negative.

Jack (moderator)- Hide quoted text -

- Show quoted text -

MY RESPONSE

THE TYPE I AND II ARE ABSOLUTELY APART THE PROBLEM IF
ONE CAN ACCEPT HE NULL HYPOTHESES BECAUSE ONE * NEVER
*
KNOW IF THE HYPOTHESES IS TRUE. TO INDICATE THAT THIS
IS FITTED TO THE PROBLEM OF ACCEPTING THE NUL
HYPOTHESES
IS OR STUPID (CIRULAR REASONING) OR AN UNETHICAL
TRICK
THE IMBECILE DUO IS SO FERTILE.

Luis Amaral Afonso [the moderator destroyer]