> power.t.test(n = NULL, delta = 26, sd = 13,
sig.level = 0.0001,
power = 0.95, type = "one.sample",
alternative = "one.sided")
One-sample t test power calculation
n = 13.74222
delta = 26
sd = 13
sig.level = 1e-04
power = 0.95
alternative = one.sided
> power.t.test(n = NULL, delta = 20, sd = 13,
sig.level = 0.0001,
power = 0.95, type = "one.sample",
alternative = "one.sided")
One-sample t test power calculation
n = 18.87723
delta = 20
sd = 13
sig.level = 1e-04
power = 0.95
alternative = one.sided
> power.t.test(n = NULL, delta = 10, sd = 13,
sig.level = 0.0001,
power = 0.95, type = "one.sample",
alternative = "one.sided")
One-sample t test power calculation
n = 55.51463
delta = 10
sd = 13
sig.level = 1e-04
power = 0.95
alternative = one.sided
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As I mentioned earlier, the way I formulate a sample size problem in the case of testing of hypothesis is what effect size is aimed to be detected (if present). The power depends on a specific value under the alternative and not more than theta or less than theta. Power requires an exact value of delta (although it may imply generalization to less than or more than). Whereas the significance level depends on the null, the power depends on the alternative. ... May I suggest to formulate the problem as to what effect is aimed to be detected?
On Wed, Feb 9, 2022 at 10:24 PM John Whittington <Joh...@mediscience.co.uk > wrote:
- At 04:44 09/02/2022, Abhaya Indrayan wrote:
- The sample size calculations done by Marc are for detecting a mean
- difference of 26, 20, and 10 respectively with a power of 95% and
- significance level 0.0001Â when SD = 13. I am not clear whether
- John's question was this. In his first case, Ho: (mean) difference <=
- 0, in the second case Ho: (mean) difference <= 20, and in the third
- case Ho: (mean) difference <= 10. Note the mean difference in the
- first case. .... I formulate a sample size problem in case of testing
- as to how much effect is to be detected (if present), denoted by delta.
- At 05:44 09/02/2022, Rich Ulrich wrote:
- I have the same problem as Abhaya. John does not describe the problem
- in a consistent way, so I was happy to see that someone made sense of it.
- The problem as stated said that (reordering) for H0s of <0, <10, and
- <20, the Ns would be 14, 56, and 19. Not good.
- As solved, it would be for deltas <26, <20, <10, yielding Ns that
- are consistent, 14, 19 and 56.
- Thanks both. This goes to show that 'simple questions' are not
- necessarily all that simple (particularly when badly formulated!),
- and perhaps explains why I felt the need to ask the question! As
- evidence of that, I'm now getting myself rather confused,and it seems
- that my problem relates to the way in which I specified the null hypotheses.
- Whether because he is a psychic or whatever, Marc's calculations
- corresponded to what I was talking about (and the calcuklations I had
- done myself) - in prose "the sample sizes needed for one to be
- ("99.99%") confident that the population mean difference was (a) >0,
- (b) >10 and (c) >20, respectively " and it stands to reason that the
- required sample sizes would increase as one moved from (a) through
- (b) to (c) - per both my and Marc's calculations It therefore seems
- that the problem is simply that I expressed the H0s incorrectly - is
- that the case?
- I have more to say/ask, but it's probably best if I first wait for
- comments at this stage before saying anything more (or making more of
- a fool of myself!).
- Kindest Regards,
- John
- On Wed, Feb 9, 2022 at 4:28 AM John Whittington
- < Joh...@mediscience.co.uk> wrote:
- >Hi Marc,
- >
- >Many thanks for your very rapid response. That sounds reassuring
- >enough for me - in fact, once one has rounded up your figures, they
- >couldn't possibly be any more reassuring :-)
- >
- >Apologies for asking such a simple question!
- >
- >Kindest Regards,
- >John
- >
- >At 22:46 08/02/2022, 'Marc Schwartz' via MedStats wrote:
- >>Hi John,
- >>
- >>Good to hear from you!
- >>
- >>Here are the three results using R:
- >>
- >> > power.t.test(n = NULL, delta = 26, sd = 13,
- >>
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 13.74222
- >>Â Â Â Â Â Â delta = 26
- >>Â Â Â Â Â Â Â sd = 13
- >>
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>Â Â Â alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 20, sd = 13,
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 18.87723
- >>Â Â Â Â Â Â delta = 20
- >>Â Â Â Â Â Â Â sd = 13
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>Â Â Â alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 10, sd = 13,
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 55.51463
- >>Â Â Â Â Â Â delta = 10
- >>Â Â Â Â Â Â Â sd = 13
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>
- >>Â Â Â alternative = one.sided
- >>
- >>
- >>So, yes, you appear to be on the right track... :-)
- >>
- >>Regards,
- >>
- >>Marc
- >>
- >>On February 8, 2022 at 5:17:16 PM, John Whittington
- >>( joh...@mediscience.co.uk) wrote:
- >>>Hi folks, I hope that all is well with you all, and all of your
- >>>'yours', and (a little belatedly) that you all have a healthy, happy
- >>>and successful 2022.
- >>>
- >>>This time, it's all pretty basic and straightforward 'sample size'
- >>>issue (essentially just t-tests), and I'm just seeking reassurance!
- >>>
- >>>I'm playing with some sample sizes estimates for paired differences
- >>>of hypothesised Normal distributions with various variances. I think
- >>>that one-sided tests are appropriate, although it actually makes very
- >>>little difference with my figures.
- >>>
- >>>With a simplified, but typical, example, I have a distribution with a
- >>>mean of 26 and an SD of 13. With that, I think that the sample size
- >>>required to give 95% power to reject H0:difference =<0 with
- >>>(one-tailed) p=0.0001 is about N=14 (differences).
- >>>
- >>>Moving on for that, what if I want the sample size to reject, say,
- >>>H0:difference =<20 (with the same parameters)? If I have got it
- >>>right, that results in a sample size estimate of about N=19 - and,
- >>>similarly, about N=56 to reject H0:difference =<10 (again, with same
- >>>parameters. Is that correct? If not, "please advise" !
- >>>
- >>>Thanks for any reassurance (and/or 'education'!).
- >>>
- >>>Kind Regards,
- >>>John
- John
- ----------------------------------------------------------------
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- Mediscience Services    Fax:   +44 (0) 1296 738893
- Twyford Manor, Twyford,  E-mail:  Joh...@mediscience.co.uk
- Buckingham MK18 4EL, UK
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A power statement has a set of parameters; you GIVE all but the one you will solve for. You GIVE a test by stating both the name of the test and the alpha.
Thus, I work from something like this. For a given test and alpha error [one-tailed t-test at p= 0.0001], what is the sample size [solve for N] required to GIVE a stated power [95%]
for this GIVEN effect size? [Three: 26/13; 20/13; 10/13]
For clinical psychiatric research, I most often provided the PI with tables for a 5% test at 80% power and 90% or 95% power, showing Ns for several effect sizes.
By the way, none of my power analyses ever used alpha smaller than 1%, but I am aware that the t and F distributions are increasingly LESS accurate in real data for alphas smaller than that.
Does this bother folks who want to use p= 0.0001?
- that the case?
- a fool of myself!).
- Kindest Regards,
- John
- < Joh...@mediscience.co.uk> wrote:
- >Hi Marc,
- >
- >
- >
- >Kindest Regards,
- >John
- >
- >>Hi John,
- >>
- >>
- >>
- >>
- >> sig.level = 0.0001,
- >> alternative = "one.sided")
- >>
- >>
- >> n = 13.74222
- >> delta = 26
- >> sd = 13
- >>
- >> sig.level = 1e-04
- >> power = 0.95
- >> alternative = one.sided
- >>
- >> sig.level = 0.0001,
- >> alternative = "one.sided")
- >>
- >>
- >> n = 18.87723
- >> delta = 20
- >> sd = 13
- >> sig.level = 1e-04
- >> power = 0.95
- >> alternative = one.sided
- >>
- >> sig.level = 0.0001,
- >> alternative = "one.sided")
- >>
- >>
- >> n = 55.51463
- >> delta = 10
- >> sd = 13
- >> sig.level = 1e-04
- >> power = 0.95
- >>
- >> alternative = one.sided
- >>
- >>
- >>
- >>Regards,
- >>
- >>Marc
- >>
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To detect a minimum mean difference delta = 10 with a power of at least 95% and significance level (one-tail) 0.0001, I get a sample size of a minimum of 49 when the SD = 13. This sample size almost surely will not miss a mean difference of 10 or more (if present) but can miss if the mean difference is less than 10.
The other point is that the sample size calculations are for detecting a specified effect when present. If not present, no big sample size will not be able to detect it. That is my understanding and I hope I am not wrong.
On Thu, Feb 10, 2022 at 10:44 AM Rich Ulrich <rich-...@live.com> wrote:
- A power statement has a set of parameters; you GIVE all but
- the one you will solve for. You GIVE a test by stating both the
- name of the test and the alpha.
- Thus, I work from something like this. Â
- For a given test and alpha error [one-tailed t-test at p= 0.0001],
- what is the sample size [solve for N]
- required to GIVE a stated power [95%]
- for this GIVEN effect size? [Three: 26/13; 20/13; 10/13]
- For clinical psychiatric research, I most often provided the PI
- with tables for a 5% test at 80% power and 90% or 95% power,
- showing Ns for several effect sizes.
- By the way, none of my power analyses ever used alpha smaller
- than 1%, but I am aware that the t and F distributions are
- increasingly LESS accurate in real data for alphas smaller than that.
- Does this bother folks who want to use p= 0.0001?
- I think the discrepancy in two-group t occurs when there are
- short-tail or long-tail distributions in the samples, which (IIRC)
- yield the opposite excesses for the t's. (On randomly generated
- samples, excess big t's arise when the 'random' standard deviations
- happen to be small.) Â I never checked what happens for one-sample t.
- --
- Rich Ulrich
- From: meds...@googlegroups.com < meds...@googlegroups.com> on behalf of Abhaya Indrayan <a.ind...@gmail.com>
- Sent: Wednesday, February 9, 2022 8:10 PM
- To: MedS...@googlegroups.com < MedS...@googlegroups.com>
- Subject: Re: {MEDSTATS} Sample Size Again!
- Â
- John:
- As I mentioned earlier, the way I formulate a sample size problem in the case of testing of hypothesis is what effect size is aimed to be detected (if present). The power depends on a specific value under the alternative and not more than theta or less than theta. Power requires an exact value of delta  (although it may imply generalization to less than or more than). Whereas the significance level depends on the null, the power depends on the alternative.
- May I suggest to formulate the problem as to what effect is aimed to be detected?
- Regards.
- ~Abhaya
- On Wed, Feb 9, 2022 at 10:24 PM John Whittington <Joh...@mediscience.co.uk > wrote:
- At 04:44 09/02/2022, Abhaya Indrayan wrote:
- The sample size calculations done by Marc are for detecting a mean
- difference of 26, 20, and 10 respectively with a power of 95% and
- significance level 0.0001Â when SD = 13. I am not clear whether
- John's question was this. In his first case, Ho: (mean) difference <=
- 0, in the second case Ho: (mean) difference <= 20, and in the third
- case Ho: (mean) difference <= 10. Note the mean difference in the
- first case. .... I formulate a sample size problem in case of testing
- as to how much effect is to be detected (if present), denoted by delta.
- At 05:44 09/02/2022, Rich Ulrich wrote:
- I have the same problem as Abhaya. John does not describe the problem
- in a consistent way, so I was happy to see that someone made sense of it.
- The problem as stated said that (reordering) for H0s of <0, <10, and
- <20, the Ns would be 14, 56, and 19. Not good.
- As solved, it would be for deltas <26, <20, <10, yielding Ns that
- are consistent, 14, 19 and 56.
- Thanks both. This goes to show that 'simple questions' are not
- necessarily all that simple (particularly when badly formulated!),
- and perhaps explains why I felt the need to ask the question! As
- evidence of that, I'm now getting myself rather confused,and it seems
- that my problem relates to the way in which I specified the null hypotheses.
- Whether because he is a psychic or whatever, Marc's calculations
- corresponded to what I was talking about (and the calcuklations I had
- done myself) - in prose "the sample sizes needed for one to be
- ("99.99%") confident that the population mean difference was (a) >0,
- (b) >10 and (c) >20, respectively " and it stands to reason that the
- required sample sizes would increase as one moved from (a) through
- (b) to (c) - per both my and Marc's calculations It therefore seems
- that the problem is simply that I expressed the H0s incorrectly - is
- that the case?
- I have more to say/ask, but it's probably best if I first wait for
- comments at this stage before saying anything more (or making more of
- a fool of myself!).
- Kindest Regards,
- John
- On Wed, Feb 9, 2022 at 4:28 AM John Whittington
- < Joh...@mediscience.co.uk> wrote:
- >Hi Marc,
- >
- >Many thanks for your very rapid response. That sounds reassuring
- >enough for me - in fact, once one has rounded up your figures, they
- >couldn't possibly be any more reassuring :-)
- >
- >Apologies for asking such a simple question!
- >
- >Kindest Regards,
- >John
- >
- >At 22:46 08/02/2022, 'Marc Schwartz' via MedStats wrote:
- >>Hi John,
- >>
- >>Good to hear from you!
- >>
- >>Here are the three results using R:
- >>
- >> > power.t.test(n = NULL, delta = 26, sd = 13,
- >>
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 13.74222
- >>Â Â Â Â Â Â delta = 26
- >>Â Â Â Â Â Â Â sd = 13
- >>
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>Â Â Â alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 20, sd = 13,
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 18.87723
- >>Â Â Â Â Â Â delta = 20
- >>Â Â Â Â Â Â Â sd = 13
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>Â Â Â alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 10, sd = 13,
- >>Â Â Â Â Â Â Â Â sig.level = 0.0001,
- >>Â Â Â Â Â Â Â Â power = 0.95, type = "one.sample",
- >>Â Â Â Â Â Â Â Â alternative = "one.sided")
- >>
- >>Â Â Â One-sample t test power calculation
- >>
- >>Â Â Â Â Â Â Â Â n = 55.51463
- >>Â Â Â Â Â Â delta = 10
- >>Â Â Â Â Â Â Â sd = 13
- >>Â Â Â Â sig.level = 1e-04
- >>Â Â Â Â Â Â power = 0.95
- >>
- >>Â Â Â alternative = one.sided
- >>
- >>
- >>So, yes, you appear to be on the right track... :-)
- >>
- >>Regards,
- >>
- >>Marc
- >>
- >>On February 8, 2022 at 5:17:16 PM, John Whittington
- >>( joh...@mediscience.co.uk) wrote:
- >>>Hi folks, I hope that all is well with you all, and all of your
- >>>'yours', and (a little belatedly) that you all have a healthy, happy
- >>>and successful 2022.
- >>>
- >>>This time, it's all pretty basic and straightforward 'sample size'
- >>>issue (essentially just t-tests), and I'm just seeking reassurance!
- >>>
- >>>I'm playing with some sample sizes estimates for paired differences
- >>>of hypothesised Normal distributions with various variances. I think
- >>>that one-sided tests are appropriate, although it actually makes very
- >>>little difference with my figures.
- >>>
- >>>With a simplified, but typical, example, I have a distribution with a
- >>>mean of 26 and an SD of 13. With that, I think that the sample size
- >>>required to give 95% power to reject H0:difference =<0 with
- >>>(one-tailed) p=0.0001 is about N=14 (differences).
- >>>
- >>>Moving on for that, what if I want the sample size to reject, say,
- >>>H0:difference =<20 (with the same parameters)? If I have got it
- >>>right, that results in a sample size estimate of about N=19 - and,
- >>>similarly, about N=56 to reject H0:difference =<10 (again, with same
- >>>parameters. Is that correct? If not, "please advise" !
- >>>
- >>>Thanks for any reassurance (and/or 'education'!).
- >>>
- >>>Kind Regards,
- >>>John
- John
- ----------------------------------------------------------------
- Dr John Whittington,    Voice:  +44 (0) 1296 730225
- Mediscience Services    Fax:   +44 (0) 1296 738893
- Twyford Manor, Twyford,  E-mail:  Joh...@mediscience.co.uk
- Buckingham MK18 4EL, UK
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The other point is that the sample size calculations are for detecting a specified effect when present. If not present, no big sample size will not be able to detect it. That is my understanding and I hope I am not wrong.
To view this discussion on the web, visit https://groups.google.com/d/msgid/medstats/202202101742.21AHg4Eg027088%40mail18c50.megamailservers.eu.
The extra 'not' was an error.
Yes, this is obvious for diffrence = 0. This is where we have to be careful. I said 'specified' effect when present. In this case this is 10 and not zero. If the diffrence in the population is less than 10, no sample size will be able to detect a diffrence of at least 10. At least that is my understanding. I would like to be wiser on this count.
- At 10:54 10/02/2022, Abhaya Indrayan wrote:
- To detect a minimum mean difference delta = 10 with a power of at least 95% and significance level (one-tail) 0.0001, I get a sample size of a minimum of 49 when the SD = 13. This sample size almost surely will not miss a mean difference of 10 or more (if present) but can miss if the mean difference is less than 10.
- Agreed, albeit Marc and myself got 56, rather than 49.
- The other point is that the sample size calculations are for detecting a specified effect when present. If not present, no big sample size will not be able to detect it. That is my understanding and I hope I am not wrong.
- That is obviously true, provided that "not present" means EXACTLY zero. If the effect is non-zero (even if incredibly close to zero) one can get whatever power one wants, with any alpha, to detect the effect if the sample size is large enough.
- For example, if one can believe my software with such extreme parameters, with our SD of 13, a power of at least 95% could be achieved to detect an effect of 0.0001 using a one-tailed t-test with alpha=0.0001 with a sample size of N = 5,178,280,000
- Kindest Regards,
- John
- On Thu, Feb 10, 2022 at 10:44 AM Rich Ulrich <rich-...@live.com> wrote:
- A power statement has a set of parameters; you GIVE all but
- the one you will solve for. You GIVE a test by stating both the
- name of the test and the alpha.
- Thus, I work from something like this. Â
- For a given test and alpha error [one-tailed t-test at p= 0.0001],
- what is the sample size [solve for N]
- required to GIVE a stated power [95%]
- for this GIVEN effect size? [Three: 26/13; 20/13; 10/13]
- For clinical psychiatric research, I most often provided the PI
- with tables for a 5% test at 80% power and 90% or 95% power,
- showing Ns for several effect sizes.
- By the way, none of my power analyses ever used alpha smaller
- than 1%, but I am aware that the t and F distributions are
- increasingly LESS accurate in real data for alphas smaller than that.
- Does this bother folks who want to use p= 0.0001?
- I think the discrepancy in two-group t occurs when there are
- short-tail or long-tail distributions in the samples, which (IIRC)
- yield the opposite excesses for the t's. (On randomly generated
- samples, excess big t's arise when the 'random' standard deviations
- happen to be small.)  I never checked what happens for one-sample t.
- --
- Rich Ulrich
- From: meds...@googlegroups.com < meds...@googlegroups.com> on behalf of Abhaya Indrayan <a.ind...@gmail.com>
- Sent: Wednesday, February 9, 2022 8:10 PM
- To: MedS...@googlegroups.com < MedS...@googlegroups.com>
- Subject: Re: {MEDSTATS} Sample Size Again!
- Â
- John:
- As I mentioned earlier, the way I formulate a sample size problem in the case of testing of hypothesis is what effect size is aimed to be detected (if present). The power depends on a specific value under the alternative and not more than theta or less than theta. Power requires an exact value of delta  (although it may imply generalization to less than or more than). Whereas the significance level depends on the null, the power depends on the alternative.
- May I suggest to formulate the problem as to what effect is aimed to be detected?
- Regards.
- ~Abhaya
- On Wed, Feb 9, 2022 at 10:24 PM John Whittington <Joh...@mediscience.co.uk > wrote:
- At 04:44 09/02/2022, Abhaya Indrayan wrote:
- The sample size calculations done by Marc are for detecting a mean
- difference of 26, 20, and 10 respectively with a power of 95% and
- significance level 0.0001 when SD = 13. I am not clear whether
- John's question was this. In his first case, Ho: (mean) difference <=
- 0, in the second case Ho: (mean) difference <= 20, and in the third
- case Ho: (mean) difference <= 10. Note the mean difference in the
- first case. .... I formulate a sample size problem in case of testing
- as to how much effect is to be detected (if present), denoted by delta.
- At 05:44 09/02/2022, Rich Ulrich wrote:
- I have the same problem as Abhaya. John does not describe the problem
- in a consistent way, so I was happy to see that someone made sense of it.
- The problem as stated said that (reordering) for H0s of <0, <10, and
- <20, the Ns would be 14, 56, and 19. Not good.
- As solved, it would be for deltas <26, <20, <10, yielding Ns that
- are consistent, 14, 19 and 56.
- Thanks both. This goes to show that 'simple questions' are not
- necessarily all that simple (particularly when badly formulated!),
- and perhaps explains why I felt the need to ask the question! As
- evidence of that, I'm now getting myself rather confused,and it seems
- that my problem relates to the way in which I specified the null hypotheses.
- Whether because he is a psychic or whatever, Marc's calculations
- corresponded to what I was talking about (and the calcuklations I had
- done myself) - in prose "the sample sizes needed for one to be
- ("99.99%") confident that the population mean difference was (a) >0,
- (b) >10 and (c) >20, respectively " and it stands to reason that the
- required sample sizes would increase as one moved from (a) through
- (b) to (c) - per both my and Marc's calculations It therefore seems
- that the problem is simply that I expressed the H0s incorrectly - is
- that the case?
- I have more to say/ask, but it's probably best if I first wait for
- comments at this stage before saying anything more (or making more of
- a fool of myself!).
- Kindest Regards,
- John
- On Wed, Feb 9, 2022 at 4:28 AM John Whittington
- < Joh...@mediscience.co.uk> wrote:
- >Hi Marc,
- >
- >Many thanks for your very rapid response. That sounds reassuring
- >enough for me - in fact, once one has rounded up your figures, they
- >couldn't possibly be any more reassuring :-)
- >
- >Apologies for asking such a simple question!
- >
- >Kindest Regards,
- >John
- >
- >At 22:46 08/02/2022, 'Marc Schwartz' via MedStats wrote:
- >>Hi John,
- >>
- >>Good to hear from you!
- >>
- >>Here are the three results using R:
- >>
- >> > power.t.test(n = NULL, delta = 26, sd = 13,
- >>
- >>        sig.level = 0.0001,
- >>        power = 0.95, type = "one.sample",
- >>        alternative = "one.sided")
- >>
- >>   One-sample t test power calculation
- >>
- >>        n = 13.74222
- >>      delta = 26
- >>       sd = 13
- >>
- >>    sig.level = 1e-04
- >>      power = 0.95
- >>   alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 20, sd = 13,
- >>        sig.level = 0.0001,
- >>        power = 0.95, type = "one.sample",
- >>        alternative = "one.sided")
- >>
- >>   One-sample t test power calculation
- >>
- >>        n = 18.87723
- >>      delta = 20
- >>       sd = 13
- >>    sig.level = 1e-04
- >>      power = 0.95
- >>   alternative = one.sided
- >>
- >> > power.t.test(n = NULL, delta = 10, sd = 13,
- >>        sig.level = 0.0001,
- >>        power = 0.95, type = "one.sample",
- >>        alternative = "one.sided")
- >>
- >>   One-sample t test power calculation
- >>
- >>        n = 55.51463
- >>      delta = 10
- >>       sd = 13
- >>    sig.level = 1e-04
- >>      power = 0.95
- >>
- >>   alternative = one.sided
- >>
- >>
- >>
- >>Regards,
- >>
- >>Marc
- >>
- >>( joh...@mediscience.co.uk) wrote:
- >>>and successful 2022.
- >>>
- >>>
- >>>
- >>>
- >>>
- >>>
- >>>Kind Regards,
- >>>John
- John
- ----------------------------------------------------------------
- Dr John Whittington,    Voice:  +44 (0) 1296 730225
- Mediscience Services    Fax:   +44 (0) 1296 738893
- Twyford Manor, Twyford,  E-mail:  Joh...@mediscience.co.uk
- Buckingham MK18 4EL, UK
- John
- ----------------------------------------------------------------
- Dr John Whittington,      Voice:   +44 (0) 1296 730225
- Mediscience Services      Fax:     +44 (0) 1296 738893
- Twyford Manor, Twyford,   E-mail:  Joh...@mediscience.co.uk
- Buckingham MK18 4EL, UK           Â
- ----------------------------------------------------------------
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