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How likely is sample A and sample B is from distribution C?


Learning to create samples from an unknown distributionDifference between null distribution and sampling distributionHow to make a two-tailed hypergeometric test?Why can variance be estimated from a sample taken from an alternative hypothesis?Is this sample drawn from the normal distribution ? using information from both mean and standard deviationInfer a population, and hence a sampling distribution, from a sampleWhen should I use one-sample t-test and when should I use t-test for two population means?How to combine probability plots and hypothesis tests to check normality?Testing if two distributions have the same mean by using a sample distributionHypothesis Testing - Switch hypothesis and get same result?






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;








2












$begingroup$


Let's say I have a sample A: [0,0,0,1]
and another sample B: [2,0,5,10,100,3,2,6]



I would like to know the probability that A and B are both picked from the same population C.



I tried applying a hypothesis test, but it gives me a p value of approx. 0.39 and I think it should be clear that it's very unlikely that both samples are from the same distribution.










share|cite|improve this question







New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$











  • $begingroup$
    I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
    $endgroup$
    – BruceET
    7 hours ago











  • $begingroup$
    As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
    $endgroup$
    – Glen_b
    1 hour ago


















2












$begingroup$


Let's say I have a sample A: [0,0,0,1]
and another sample B: [2,0,5,10,100,3,2,6]



I would like to know the probability that A and B are both picked from the same population C.



I tried applying a hypothesis test, but it gives me a p value of approx. 0.39 and I think it should be clear that it's very unlikely that both samples are from the same distribution.










share|cite|improve this question







New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$











  • $begingroup$
    I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
    $endgroup$
    – BruceET
    7 hours ago











  • $begingroup$
    As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
    $endgroup$
    – Glen_b
    1 hour ago














2












2








2





$begingroup$


Let's say I have a sample A: [0,0,0,1]
and another sample B: [2,0,5,10,100,3,2,6]



I would like to know the probability that A and B are both picked from the same population C.



I tried applying a hypothesis test, but it gives me a p value of approx. 0.39 and I think it should be clear that it's very unlikely that both samples are from the same distribution.










share|cite|improve this question







New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$




Let's say I have a sample A: [0,0,0,1]
and another sample B: [2,0,5,10,100,3,2,6]



I would like to know the probability that A and B are both picked from the same population C.



I tried applying a hypothesis test, but it gives me a p value of approx. 0.39 and I think it should be clear that it's very unlikely that both samples are from the same distribution.







probability hypothesis-testing distributions p-value multivariate-analysis






share|cite|improve this question







New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.










share|cite|improve this question







New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.








share|cite|improve this question




share|cite|improve this question






New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.








asked 8 hours ago









Franc WeserFranc Weser

113 bronze badges




113 bronze badges




New contributor



Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.




New contributor




Franc Weser is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.













  • $begingroup$
    I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
    $endgroup$
    – BruceET
    7 hours ago











  • $begingroup$
    As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
    $endgroup$
    – Glen_b
    1 hour ago

















  • $begingroup$
    I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
    $endgroup$
    – BruceET
    7 hours ago











  • $begingroup$
    As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
    $endgroup$
    – Glen_b
    1 hour ago
















$begingroup$
I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
$endgroup$
– BruceET
7 hours ago





$begingroup$
I'm guessing you used a pooled 2-sample t test, which is not a good choice here because sample sizes are small, 100 is a far outlier, and sample variances are hugely different. But your intuition that these data are not likely to have come from the same population is correct.
$endgroup$
– BruceET
7 hours ago













$begingroup$
As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
$endgroup$
– Glen_b
1 hour ago





$begingroup$
As phrased the question (which contains a request for a probability), appears to be framed as a Bayesian problem. I expect that a Bayesian analysis is likely not the OP's intent, but if answers talk about hypothesis tests they should also discuss what question those answer (in place of what the question asks).
$endgroup$
– Glen_b
1 hour ago











1 Answer
1






active

oldest

votes


















2












$begingroup$

You don't say what kind of hypothesis test you used.
Doing inference on such small samples as these is always
going to be difficult. However, a nonparametric Kolmogorov-Smirnov test (in R) does reject the null hypothesis that these
two samples were randomly sampled from the same population.



There is a warning message that (on account of the ties), the P-value is not exact, but 0.034 seems sufficiently smaller than 0.05 to say that we can reject at the 5% level.



x1 = c(0,0,0,1)
x2 = c(2,0,5,10,100,3,2,6)
ks.test(x1, x2)

Two-sample Kolmogorov-Smirnov test

data: x1 and x2
D = 0.875, p-value = 0.0337
alternative hypothesis: two-sided

Warning message:
In ks.test(x1, x2) : cannot compute exact p-value with ties


Similar data without ties gives a 'cleaner' test--rejecting the null hypothesis with no warning messages.



y1 = c(.01, .02, .03, .9)
y2 = c(2,0,5,10,100,3,2.1,6)
ks.test(y1, y2)

Two-sample Kolmogorov-Smirnov test

data: y1 and y2
D = 0.875, p-value = 0.0202
alternative hypothesis: two-sided


Another possible test is the two-sample Wilcoxon (rank sum test). Its distribution theory is also somewhat disturbed by ties, but it does find a significant difference between your two samples. Looking just at the P-value, we have:



wilcox.test(x1,x2)$p.val
[1] 0.02434338
Warning message:
In wilcox.test.default(x1, x2) :
cannot compute exact p-value with ties





share|cite|improve this answer











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    1 Answer
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    1 Answer
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    active

    oldest

    votes






    active

    oldest

    votes









    2












    $begingroup$

    You don't say what kind of hypothesis test you used.
    Doing inference on such small samples as these is always
    going to be difficult. However, a nonparametric Kolmogorov-Smirnov test (in R) does reject the null hypothesis that these
    two samples were randomly sampled from the same population.



    There is a warning message that (on account of the ties), the P-value is not exact, but 0.034 seems sufficiently smaller than 0.05 to say that we can reject at the 5% level.



    x1 = c(0,0,0,1)
    x2 = c(2,0,5,10,100,3,2,6)
    ks.test(x1, x2)

    Two-sample Kolmogorov-Smirnov test

    data: x1 and x2
    D = 0.875, p-value = 0.0337
    alternative hypothesis: two-sided

    Warning message:
    In ks.test(x1, x2) : cannot compute exact p-value with ties


    Similar data without ties gives a 'cleaner' test--rejecting the null hypothesis with no warning messages.



    y1 = c(.01, .02, .03, .9)
    y2 = c(2,0,5,10,100,3,2.1,6)
    ks.test(y1, y2)

    Two-sample Kolmogorov-Smirnov test

    data: y1 and y2
    D = 0.875, p-value = 0.0202
    alternative hypothesis: two-sided


    Another possible test is the two-sample Wilcoxon (rank sum test). Its distribution theory is also somewhat disturbed by ties, but it does find a significant difference between your two samples. Looking just at the P-value, we have:



    wilcox.test(x1,x2)$p.val
    [1] 0.02434338
    Warning message:
    In wilcox.test.default(x1, x2) :
    cannot compute exact p-value with ties





    share|cite|improve this answer











    $endgroup$

















      2












      $begingroup$

      You don't say what kind of hypothesis test you used.
      Doing inference on such small samples as these is always
      going to be difficult. However, a nonparametric Kolmogorov-Smirnov test (in R) does reject the null hypothesis that these
      two samples were randomly sampled from the same population.



      There is a warning message that (on account of the ties), the P-value is not exact, but 0.034 seems sufficiently smaller than 0.05 to say that we can reject at the 5% level.



      x1 = c(0,0,0,1)
      x2 = c(2,0,5,10,100,3,2,6)
      ks.test(x1, x2)

      Two-sample Kolmogorov-Smirnov test

      data: x1 and x2
      D = 0.875, p-value = 0.0337
      alternative hypothesis: two-sided

      Warning message:
      In ks.test(x1, x2) : cannot compute exact p-value with ties


      Similar data without ties gives a 'cleaner' test--rejecting the null hypothesis with no warning messages.



      y1 = c(.01, .02, .03, .9)
      y2 = c(2,0,5,10,100,3,2.1,6)
      ks.test(y1, y2)

      Two-sample Kolmogorov-Smirnov test

      data: y1 and y2
      D = 0.875, p-value = 0.0202
      alternative hypothesis: two-sided


      Another possible test is the two-sample Wilcoxon (rank sum test). Its distribution theory is also somewhat disturbed by ties, but it does find a significant difference between your two samples. Looking just at the P-value, we have:



      wilcox.test(x1,x2)$p.val
      [1] 0.02434338
      Warning message:
      In wilcox.test.default(x1, x2) :
      cannot compute exact p-value with ties





      share|cite|improve this answer











      $endgroup$















        2












        2








        2





        $begingroup$

        You don't say what kind of hypothesis test you used.
        Doing inference on such small samples as these is always
        going to be difficult. However, a nonparametric Kolmogorov-Smirnov test (in R) does reject the null hypothesis that these
        two samples were randomly sampled from the same population.



        There is a warning message that (on account of the ties), the P-value is not exact, but 0.034 seems sufficiently smaller than 0.05 to say that we can reject at the 5% level.



        x1 = c(0,0,0,1)
        x2 = c(2,0,5,10,100,3,2,6)
        ks.test(x1, x2)

        Two-sample Kolmogorov-Smirnov test

        data: x1 and x2
        D = 0.875, p-value = 0.0337
        alternative hypothesis: two-sided

        Warning message:
        In ks.test(x1, x2) : cannot compute exact p-value with ties


        Similar data without ties gives a 'cleaner' test--rejecting the null hypothesis with no warning messages.



        y1 = c(.01, .02, .03, .9)
        y2 = c(2,0,5,10,100,3,2.1,6)
        ks.test(y1, y2)

        Two-sample Kolmogorov-Smirnov test

        data: y1 and y2
        D = 0.875, p-value = 0.0202
        alternative hypothesis: two-sided


        Another possible test is the two-sample Wilcoxon (rank sum test). Its distribution theory is also somewhat disturbed by ties, but it does find a significant difference between your two samples. Looking just at the P-value, we have:



        wilcox.test(x1,x2)$p.val
        [1] 0.02434338
        Warning message:
        In wilcox.test.default(x1, x2) :
        cannot compute exact p-value with ties





        share|cite|improve this answer











        $endgroup$



        You don't say what kind of hypothesis test you used.
        Doing inference on such small samples as these is always
        going to be difficult. However, a nonparametric Kolmogorov-Smirnov test (in R) does reject the null hypothesis that these
        two samples were randomly sampled from the same population.



        There is a warning message that (on account of the ties), the P-value is not exact, but 0.034 seems sufficiently smaller than 0.05 to say that we can reject at the 5% level.



        x1 = c(0,0,0,1)
        x2 = c(2,0,5,10,100,3,2,6)
        ks.test(x1, x2)

        Two-sample Kolmogorov-Smirnov test

        data: x1 and x2
        D = 0.875, p-value = 0.0337
        alternative hypothesis: two-sided

        Warning message:
        In ks.test(x1, x2) : cannot compute exact p-value with ties


        Similar data without ties gives a 'cleaner' test--rejecting the null hypothesis with no warning messages.



        y1 = c(.01, .02, .03, .9)
        y2 = c(2,0,5,10,100,3,2.1,6)
        ks.test(y1, y2)

        Two-sample Kolmogorov-Smirnov test

        data: y1 and y2
        D = 0.875, p-value = 0.0202
        alternative hypothesis: two-sided


        Another possible test is the two-sample Wilcoxon (rank sum test). Its distribution theory is also somewhat disturbed by ties, but it does find a significant difference between your two samples. Looking just at the P-value, we have:



        wilcox.test(x1,x2)$p.val
        [1] 0.02434338
        Warning message:
        In wilcox.test.default(x1, x2) :
        cannot compute exact p-value with ties






        share|cite|improve this answer














        share|cite|improve this answer



        share|cite|improve this answer








        edited 7 hours ago

























        answered 7 hours ago









        BruceETBruceET

        9,5581 gold badge8 silver badges24 bronze badges




        9,5581 gold badge8 silver badges24 bronze badges




















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