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Thread: 2 sided exact significance in fisher's exact test

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    2 sided exact significance in fisher's exact test



    i compared the frequency of cardioembolism between age-group <45years and 46-50 years using SPSS using cross-tab. As you can see the result in the picture, should i have to select the 2 sided exact significance?

    please help me.
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    Re: 2 sided exact significance in fisher's exact test


    The selection of one-sided or two sided test depends on what your objective is, what question you trying to answer. Most importantly, you should have set/defined those objectives before collecting your data [rather than contemplating what tests to use afterwards].

    First of all, it is good that you are using a fisher exact test in this situation as you are in a small sample situation where the chi-squared approximation doesn't perform very well.

    To delve a bit more into the details. If your objective is to detect the departure from the null hypothesis in either direction, then a two-sided test is appropriate. The null hypothesis is that the true Odds Ratio (OR)=1. If you want to be able to detect if your OR is <1 or >1, then a one-sided test would be appropriate.

    Your two-sided test shows that the true OR is significantly less than 1 at 10% level of significance [but not at 5% level].

    Your estimated OR is 0.13 and associated 95% CI is (0.00-1.23). As you can see your OR contains 1, hence, the significance is marginal at 5% level.

    Code: 
    
    ## Two-Sided test
            Fisher's Exact Test for Count Data
    
    data:  mydata 
    p-value = 0.08081
    alternative hypothesis: true odds ratio is not equal to 1 
    95 percent confidence interval:
     0.002707774 1.229330884 
    sample estimates:
    odds ratio 
     0.1315444
    If your interest was to test that true odds ratio is <1 i.e. if you want to detect the departure from the null in only direction (one-tail only), then your test is significant at 5% level.
    The OR is 0.13 and 95% CI is:0.00-0.95, which doesn't contain 1.

    Code: 
    > fisher.test(mydata,alternative="less")
    
            Fisher's Exact Test for Count Data
    
    data:  mydata 
    p-value = 0.04404
    alternative hypothesis: true odds ratio is less than 1 
    95 percent confidence interval:
     0.0000000 0.9519217 
    sample estimates:
    odds ratio 
     0.1315444
    HTH
    Oh Thou Perelman! Poincare's was for you and Riemann's is for me.

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