I would like to know whether Blood Pressure is significant in contributing to a diseaseHypo. I have data from three different continents(let's say A,B,C) and each with groups diseased and not_diseased. I have attempted to do t-test in R t.test() and wondering whether I would need to assume that there is a equal variance or not between diseased(diseased_A + diseased_B + diseased_C) and not_diseased(not_diseased_A + not_diseased_B + not_diseased_C) of combined population.

Any views in this scenario please ?


  • $\begingroup$ Do you wonder if BP contributes to disease, or if BP differs by disease? These are not the same. $\endgroup$ – gung Jun 4 '15 at 12:53
  • $\begingroup$ I would like to see whether BP contributes to the disease. Should I consider the sample data from different demographies separately or together, what are the impacts and what assumptions needs to be made ? What should I assume on the variance on the combined data, equal or unequal?? $\endgroup$ – Prradep Jun 4 '15 at 14:29
  • $\begingroup$ If there might be differences among continents in the relation of Blood Pressure to diseaseHypo, you need to consider continent as a factor in your analysis: ANOVA instead of a simple t-test, or (if disease presence is clearly a yes/no classification) a logistic regression of diseaseHypo against Blood Pressure, continent, and any other variables of interest. $\endgroup$ – EdM Jun 4 '15 at 19:41
  • $\begingroup$ Yeah, that's what my plan is. Before that, I want to choose variables from list of 100+ variables which have significant contribution to the disease. For this identification, I am doing t-test between each variable and disease status. Now, how do I consider the variance equal or unequal ? I have no prior information on variance. In another words, to use Welch test or student t-test ? $\endgroup$ – Prradep Jun 4 '15 at 19:55
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    $\begingroup$ The big issue here is not which test to use; based on your latest comment, it's that choosing individual variables for later analysis in this way is not a good approach to this problem. There is a very large literature on the problem of variable selection; follow the feature-selection and similar tags on this web site, and study references like An Introduction to Statistical Learning. $\endgroup$ – EdM Jun 8 '15 at 13:52

I think your main question is whether to assume equal variance or not. You can test for difference in variance by F-test: http://en.wikipedia.org/wiki/F-test_of_equality_of_variances

Also you can to your t.test using both equal and then unequal variance option and see if the results are different. If the results are same, then there is no issue.

  • $\begingroup$ Thanks. Can you also advise what other assumptions needs to be made when dealing with data from different demographics ? $\endgroup$ – Prradep Jun 8 '15 at 11:41

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