Hi All,
Here is the context: a lot of texts for hypothesis testing/ANOVA first examine
the cases where either the number of samples per group are the same
(n1 = n2 = n3 = ...= nn) or the variances for each group are comparable
(s1 = s2 = s3 =... = sn). And a lot of times, I see "Conduct the test and then
check to see if the variance assumption is violated." If it is, then do something
else... My question is why not just go with the more robust test (nonbalanced/
nonequal variance)from the start. Wouldn't the tests naturally converge to the
same results of the standard test (balanced/equal variance)? Are there cases where
this does not happen?
RK
Here is the context: a lot of texts for hypothesis testing/ANOVA first examine
the cases where either the number of samples per group are the same
(n1 = n2 = n3 = ...= nn) or the variances for each group are comparable
(s1 = s2 = s3 =... = sn). And a lot of times, I see "Conduct the test and then
check to see if the variance assumption is violated." If it is, then do something
else... My question is why not just go with the more robust test (nonbalanced/
nonequal variance)from the start. Wouldn't the tests naturally converge to the
same results of the standard test (balanced/equal variance)? Are there cases where
this does not happen?
RK