multcompare and ttest2

2016 3 2
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更新时间：2019 1 21
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Dear Friends,
I have a simple and very basic question regarding the P-value of multcompare and ttest2 function. As i understand, after anova we use PostHoc analysis to p-value between all pairs. I expected to have the same p-value using ttest2. BUT, their p-value is very different! would you please to help me to figure out the problem? thanks Karlo

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the cyclist
the cyclist 2016-3-2
编辑：the cyclist 2016-3-2

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When you make pairwise comparisons among several groups (not just two), you are more likely to find a difference between a given pair, just by random chance.
The P-values you get from multcompare take into account. The P-values you get from running all the t-tests independently does not (because it doesn't "know" that you have run multiple comparisons.)
Relevant: XKCD comic on multiple comparisons

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karlo gonzales
karlo gonzales 2016-3-2
Thanks for your answer. please let me explain my problem with an example:
I have 4 group and one measure values.
[p,tbl,stats]=anova1(A);
[c,m,h,gnames] = multcompare(stats,'ctype','bonferroni', 'display','on')
'c(:,6)' gives a P=0.0225 between group 1 and 2.
However,
[h,p_val,ci,stats] = ttest2(A(:,1), A(:,2)); give p_val= 0.0085, i was thinking the corrected value would be p_val*4= 0.034, which is different from c(1,6).
why these values are different?
thanks again, karlo
the cyclist
the cyclist 2016-3-2
Why do you expect the multcompare P-value to be
p_val*4 = 0.034
? Do you have a reference for that expectation?
karlo gonzales
karlo gonzales 2016-3-2
I thought multiplying P_value by 4(number of groups) would be Bonferroni correction for the independent t-test. it sounds i am wrong. would you please tell me how this multiple comparison correction has to be done?
Many many thanks for your time and patient with my naive questions
the cyclist
the cyclist 2016-3-3
There are many possible solutions to the multiple comparisons problem. I don't know the algorithm that multcompare uses, and can't dig into it right now. There are references in the documentation. You could also type
edit multcompare
to see what the code does.
Dear the cyclist,
You are correct that uncorrected multiple comparisons may lead to false positives (by chance). However, when using "multcompare" with the 'lsd' correction i(e, non-corrected ttest), I still get p-values that differ from those obtained from a standard ttest (not very different, but still). Do you have any idea as to why this happens?
Felix

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