gt; >
> > --
> > Gregory (Greg) L. Snow Ph.D.
> > Statistical Data Center
> > Intermountain Healthcare
> > greg.s...@imail.org
> > 801.408.8111
> >
> >
> >> -----Original Message-
> >> From: Ted Harding [mailto:ted.hard...@manc
iginal Message-
>> From: Ted Harding [mailto:ted.hard...@manchester.ac.uk]
>> Sent: Thursday, September 02, 2010 3:59 PM
>> To: Greg Snow
>> Cc: r-help@r-project.org; Kay Cecil Cichini
>> Subject: Re: [R] general question on binomial test / sign test
>>
&
Sent: Thursday, September 02, 2010 3:59 PM
> To: Greg Snow
> Cc: r-help@r-project.org; Kay Cecil Cichini
> Subject: Re: [R] general question on binomial test / sign test
>
> On 02-Sep-10 18:01:55, Greg Snow wrote:
> > Just to add to Ted's addition to my response. I
On 02-Sep-10 18:01:55, Greg Snow wrote:
> Just to add to Ted's addition to my response. I think you are moving
> towards better understanding (and your misunderstandings are common),
> but to further clarify:
> [Wise words about P(A|B), P(B|A), P-values, etc., snipped]
>
> The real tricky bit abo
ta Center
> > Intermountain Healthcare
> > greg.s...@imail.org
> > 801.408.8111
> >
> >
> >> -Original Message-
> >> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> >> project.org] On Behalf Of Kay Cecil Ci
Cichini
Sent: Thursday, September 02, 2010 6:40 AM
To: ted.hard...@manchester.ac.uk
Cc: r-help@r-project.org
Subject: Re: [R] general question on binomial test / sign test
thanks a lot for the elaborations.
your explanations clearly brought to me that either
binom.test(1,1,0.5,"two-sided
ember 02, 2010 6:40 AM
> To: ted.hard...@manchester.ac.uk
> Cc: r-help@r-project.org
> Subject: Re: [R] general question on binomial test / sign test
>
>
> thanks a lot for the elaborations.
>
> your explanations clearly brought to me that either
> binom.test(1,1,0.5,"
..i'd like to add that i actually wanted to test the location of differences
of paired samples coming from an non-normal asymetric distribution. the
alternative hypothesis was that negative differences are more often than in
0.5 of all cases. thus i tested
(x=nr.diff.under.0,n=all.diffs,0.5,altern
thanks a lot for the elaborations.
your explanations clearly brought to me that either
binom.test(1,1,0.5,"two-sided") or binom.test(0,1,0.5) giving a
p-value of 1 simply indicate i have abolutely no ensurance to reject H0.
considering binom.test(0,1,0.5,alternative="greater") and
binom.
You state: "in reverse the p-value of 1 says that i can 100% sure
that the estimate of 0.5 is true". This is where your logic about
significance tests goes wrong.
The general logic of a singificance test is that a test statistic
(say T) is chosen such that large values represent a discrepancy
betw
i test the null that the coin is fair (p(succ) = p(fail) = 0.5) with
one trail and get a p-value of 1. actually i want to proof the
alternative H that the estimate is different from 0.5, what certainly
can not be aproven here. but in reverse the p-value of 1 says that i
can 100% sure that t
Try thinking this one through from first principles, you are essentially saying
that your null hypothesis is that you are flipping a fair coin and you want to
do a 2-tailed test. You then flip the coin exactly once, what do you expect to
happen? The p-value of 1 just means that what you saw wa
12 matches
Mail list logo
