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What statistical test can I use to compare two ratios from two independent samples. The ratios are after to before results. I need to compare the after/before ratios for two independent models and show whether they are have significant difference or not. Please help!

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  • $\begingroup$ How are you quantifying the results? Numbers of events, or continuous measurements (on a scale with a true zero, i hope) or...? $\endgroup$ – onestop Oct 22 '10 at 7:39
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    $\begingroup$ @pom I hope you can clarify this query. In addition to the issues raised by @onestop, your modification of both "samples" and "models" by the word "independent" makes one wonder about your precise meaning and your synonymous use of "ratio" and "difference" in the same sentence raises questions about what you mean by those words. One possible interpretation is that each sample consists of a set of ratios; another interpretation is that you are estimating some kind of statistic in each sample, taking the ratio of those two numbers, and want to compare it to some standard value (such as 1.0). $\endgroup$ – whuber Oct 22 '10 at 15:13
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In response to an old question, and given that a good response has been provided already elsewhere by jbowman and StasK to a very similar (but better defined) problem. I refer anyone who stumbles on this to the following question (and answers): Test for significant difference in ratios of normally distributed random variables

The permutations test should be easy to implement in most statistical tools and many programming languages. Additionally, it doesn't assume that you have count data but means that you can use a ratio of rates or other appropriate metrics.

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Any test for independence of a 2x2 contingency table will do! A chi-square or t-test are the textbook simple solutions. The "best" test in this situation is called Barnard's test for superiority -- the StatXact software will happily calculate this for you.

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    $\begingroup$ IMHO t-test may not be a good idea here $\endgroup$ – suncoolsu Oct 22 '10 at 6:52

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