Correction procedure
Dear Donald Burrill, Thank you for the reply to me. Sorry I wrote by mistake "My aim was to compare groups 2, 3, 4 with control(group 1)", would be: My aim was to compare groups 2, 3, 4,5 with control(group 1)". Anyway you had corrected it. The rewiever had written me: "Accordingly, a "statistical penalty" needs to be paid in order to account for the increased risk of a Type 1 error due to multiple comparisons. The easist way to achieve this goal to adjust the P value require to declare significance using the Bonferroni correction." 1.What is the correct meaning of the last sentence? What must I do? As you wrote, must I find the adjusted p values or declare the adjusted significance level alpha? 2. There are apparently and exactly three groups; groups 1, 3, 5 that had the same proportions of translocation. Therefore, to compare only the group 2 and 3 with the control can be appropriate, can it be? Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 =0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Correction procedure
1. Adjust the alpha level for the individual tests. i.e. if the 'family' (overall) error rate is 5%, then alpha for the individual tests is 1.25%. (Note: the Bonferroni method is conservative, 'Dunnett's test' can be used to compare a number of treatments with a control. It's in Minitab, but I don't know what it involves exactly.) 2. No. You should adjust for all 4 tests. Bekir <[EMAIL PROTECTED]> wrote in message [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > Dear Donald Burrill, > > Thank you for the reply to me. > Sorry I wrote by mistake "My aim was to compare groups 2, 3, 4 with > control(group 1)", would be: My aim was to compare groups 2, 3, 4,5 > with control(group 1)". Anyway you had corrected it. > > The rewiever had written me: "Accordingly, a "statistical penalty" > needs to be paid in order to account for the increased risk of a Type > 1 error due to multiple comparisons. The easist way to achieve this > goal to adjust the P value require to declare significance using the > Bonferroni correction." > > 1.What is the correct meaning of the last sentence? What must I do? > As you wrote, must I find the adjusted p values or declare the > adjusted significance level alpha? > > 2. There are apparently and exactly three groups; groups 1, 3, 5 that > had the same proportions of translocation. Therefore, to compare only > the group 2 and 3 with the control can be appropriate, can it be? > Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 > =0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Correction procedure
On 3 Jun 2001, Bekir wrote, in part: > My aim was to compare groups 2, 3, 4, 5 with control (group 1)". ... > > The rewiever had written me: "Accordingly, a "statistical penalty" > needs to be paid in order to account for the increased risk of a Type > 1 error due to multiple comparisons. The easist way to achieve this > goal to adjust the P value require to declare significance using the > Bonferroni correction." > > 1. What is the correct meaning of the last sentence? What must I do? > As you wrote, must I find the adjusted p values or declare the > adjusted significance level alpha? Your choice: the two ways of approaching the problem are equivalent. Either divide the criterion significance level (alpha) by the number of comparisons, as Duncan Smith recommended, and compare the p-values reported by your statistical routine to this adjusted value; or adjust the p-values by multiplying the reported values by the number of comparisons. Thus p = 0.02 > adjusted alpha = 0.125 for one of your comparisons, or adjusted p = 0.08 > nominal alpha = 0.05. If I understand your reviewer correctly, (s)he seems to be requesting the latter: adjusting the p-value. > 2. There are apparently and exactly three groups; groups 1, 3, 5 that > had the same proportions of translocation. Wasn't it groups 1, 4, 5 that had the same proportions? > Therefore, to compare only the group 2 and 3 with the control can be > appropriate, can it be? Such a comparison may be appropriate (but see below); but this does not change the situation. Had groups 4 and 5 NOT had proportion equal to group 1, you would surely have wanted to make those two comparisons also. The question is not, how many comparisons were useful or significant; but how many comparisons would you have chosen to consider before you observed the results of this particular experiment. By your description, you certainly considered AT LEAST the four comparisons mentioned in your first paragraph above. > Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 > = 0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? As explained above, and as Duncan Smith responded, No. Duncan mentioned Dunnett's test. This might indeed be appropriate for your design, but not for the analyses you have so far done. Dunnett's test would normally follow the finding of a significant F value in a one-way analysis of variance (testing the formal hypothesis that the true (population) proportions in the five groups are all identical. Such an analysis could be undertaken with your data, but some persons (possibly including your reviewer? I don't know) would object to carrying out an analysis of variance (ANOVA) with dichotomous data. One advantage to ANOVA is the possibility of drawing conclusions more complex, and possibly more interesting, than the pairwise comparisons that you had originally envisioned. In particular, you could test the contrast between Groups 2 and 3 combined, with Groups 1, 4, and 5 combined; since it seems clear that this is the only thing that is going on in your data. Testing that contrast by the Scheffe' method, which offers experimentwise protection against the null hypotheses for any imaginable contrast, might be useful: that contrast, involving all 100 cases, is more powerfully tested than the series of pairwise comparisons, and may well be significant even against the conservative Scheffe' criterion. Whether that is useful _for_your_purposes_ is another matter entirely. If there is some useful meaning and interpretation to be gained in observing that only groups 2 and 3 differ from the control group and that groups 4 and 5 are indistinguishable from the control group, then this contrast would be useful to test formally. If that outcome does not lend itself to useful interpretation (and the advance of knowledge in the field), you would probably be better off staying with the four pairwise comparisons you started with. Donald F. Burrill [EMAIL PROTECTED] 348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED] MSC #29, Plymouth, NH 03264 603-535-2597 184 Nashua Road, Bedford, NH 03110 603-471-7128 = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Correction procedure
Bekir <[EMAIL PROTECTED]> wrote: > 2. There are apparently and exactly three groups; groups 1, 3, 5 that > had the same proportions of translocation. Therefore, to compare only > the group 2 and 3 with the control can be appropriate, can it be? > Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 > =0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? No, that's not appropriate, because the fact that groups 3 and 5 had the same proportions is a contingency of your experimental results, not something that is _a priori_ true, and therefore the (zero) difference in proportion between them is subject to as much error as any of your other results. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Correction procedure
Dear Eric Bohlman, Thank you for the explanation that have I benefited much from. [EMAIL PROTECTED] (Eric Bohlman) wrote in message news:<9ffb3t$kqh$[EMAIL PROTECTED]>... > Bekir <[EMAIL PROTECTED]> wrote: > > 2. There are apparently and exactly three groups; groups 1, 3, 5 that > > had the same proportions of translocation. Therefore, to compare only > > the group 2 and 3 with the control can be appropriate, can it be? > > Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 > > =0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? > > No, that's not appropriate, because the fact that groups 3 and 5 had the > same proportions is a contingency of your experimental results, not > something that is _a priori_ true, and therefore the (zero) difference in > proportion between them is subject to as much error as any of your other > results. = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
Re: Correction procedure
Dear Duncan Smith, Thank you for the reply that was very helpful. "Duncan Smith" <[EMAIL PROTECTED]> wrote in message news:<9fdp1s$2fp$[EMAIL PROTECTED]>... > 1. Adjust the alpha level for the individual tests. i.e. if the 'family' > (overall) error rate is 5%, then alpha for the individual tests is 1.25%. > (Note: the Bonferroni method is conservative, 'Dunnett's test' can be used > to compare a number of treatments with a control. It's in Minitab, but I > don't know what it involves exactly.) > > 2. No. You should adjust for all 4 tests. > > > Bekir <[EMAIL PROTECTED]> wrote in message > [EMAIL PROTECTED]">news:[EMAIL PROTECTED]... > > Dear Donald Burrill, > > > > Thank you for the reply to me. > > Sorry I wrote by mistake "My aim was to compare groups 2, 3, 4 with > > control(group 1)", would be: My aim was to compare groups 2, 3, 4,5 > > with control(group 1)". Anyway you had corrected it. > > > > The rewiever had written me: "Accordingly, a "statistical penalty" > > needs to be paid in order to account for the increased risk of a Type > > 1 error due to multiple comparisons. The easist way to achieve this > > goal to adjust the P value require to declare significance using the > > Bonferroni correction." > > > > 1.What is the correct meaning of the last sentence? What must I do? > > As you wrote, must I find the adjusted p values or declare the > > adjusted significance level alpha? > > > > 2. There are apparently and exactly three groups; groups 1, 3, 5 that > > had the same proportions of translocation. Therefore, to compare only > > the group 2 and 3 with the control can be appropriate, can it be? > > Thus, there would be two comparisons and the p valus 0.008 (0.008 x 2 > > =0.016) and 0.02 (0.02 x 2 = 0.04)would be significant. Is it right? = Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =
