Bayes factor vs P value Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar ManaraWhen should I be worried about the Jeffreys-Lindley paradox in Bayesian model choice?Bayesian analysis and Lindley's paradox?Do Bayes factors require multiple comparison correction?When does it make sense to reject/accept an hypothesis?Why are 0.05 < p < 0.95 results called false positives?Marginal Likelihoods for Bayes Factors with Multiple Discrete HypothesisIs p-value essentially useless and dangerous to use?Are smaller p-values more convincing?Interpreting Granger Causality F-testBayes factor (B) vs p-values: sensitive (H0/H1) vs insensitive dataWald test and LRT arriving at different conclusionsCompute Bayesian Probability

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Bayes factor vs P value



Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar ManaraWhen should I be worried about the Jeffreys-Lindley paradox in Bayesian model choice?Bayesian analysis and Lindley's paradox?Do Bayes factors require multiple comparison correction?When does it make sense to reject/accept an hypothesis?Why are 0.05 < p < 0.95 results called false positives?Marginal Likelihoods for Bayes Factors with Multiple Discrete HypothesisIs p-value essentially useless and dangerous to use?Are smaller p-values more convincing?Interpreting Granger Causality F-testBayes factor (B) vs p-values: sensitive (H0/H1) vs insensitive dataWald test and LRT arriving at different conclusionsCompute Bayesian Probability



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








3












$begingroup$


I am trying to understand Bayes Factor (BF). I believe they are like likelihood ratio of 2 hypotheses. So if BF is 5, it means H1 is 5 times more likely than H0. And value of 3-10 indicates moderate evidence, while >10 indicates strong evidence.



However, for P-value, traditionally 0.05 is taken as cut-off. At this P value, H1/H0 likelihood should be 95/5 or 19.



So why a cut-off of >3 is taken for BF while a cut-off of >19 is taken for P values? These values are not anywhere close either.



I may be missing something very basic since I am a beginner in this area.










share|cite|improve this question











$endgroup$


















    3












    $begingroup$


    I am trying to understand Bayes Factor (BF). I believe they are like likelihood ratio of 2 hypotheses. So if BF is 5, it means H1 is 5 times more likely than H0. And value of 3-10 indicates moderate evidence, while >10 indicates strong evidence.



    However, for P-value, traditionally 0.05 is taken as cut-off. At this P value, H1/H0 likelihood should be 95/5 or 19.



    So why a cut-off of >3 is taken for BF while a cut-off of >19 is taken for P values? These values are not anywhere close either.



    I may be missing something very basic since I am a beginner in this area.










    share|cite|improve this question











    $endgroup$














      3












      3








      3





      $begingroup$


      I am trying to understand Bayes Factor (BF). I believe they are like likelihood ratio of 2 hypotheses. So if BF is 5, it means H1 is 5 times more likely than H0. And value of 3-10 indicates moderate evidence, while >10 indicates strong evidence.



      However, for P-value, traditionally 0.05 is taken as cut-off. At this P value, H1/H0 likelihood should be 95/5 or 19.



      So why a cut-off of >3 is taken for BF while a cut-off of >19 is taken for P values? These values are not anywhere close either.



      I may be missing something very basic since I am a beginner in this area.










      share|cite|improve this question











      $endgroup$




      I am trying to understand Bayes Factor (BF). I believe they are like likelihood ratio of 2 hypotheses. So if BF is 5, it means H1 is 5 times more likely than H0. And value of 3-10 indicates moderate evidence, while >10 indicates strong evidence.



      However, for P-value, traditionally 0.05 is taken as cut-off. At this P value, H1/H0 likelihood should be 95/5 or 19.



      So why a cut-off of >3 is taken for BF while a cut-off of >19 is taken for P values? These values are not anywhere close either.



      I may be missing something very basic since I am a beginner in this area.







      hypothesis-testing bayesian p-value






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited 2 hours ago







      rnso

















      asked 3 hours ago









      rnsornso

      4,067103168




      4,067103168




















          2 Answers
          2






          active

          oldest

          votes


















          3












          $begingroup$

          A few things:



          The BF gives you evidence in favor of a hypothesis, while a frequentist hypothesis test gives you evidence against a (null) hypothesis. So it's kind of "apples to oranges."



          These two procedures, despite the difference in interpretations, may lead to different decisions. For example, a BF might reject while a frequentist hypothesis test doesn't, or vice versa. This problem is often referred to as the Jeffreys-Lindley's paradox. There have been many posts on this site about this; see e.g. here, and here.



          "At this P value, H1/H0 likelihood should be 95/5 or 19." No, this isn't true because, roughly $p(y mid H_1) neq 1- p(y mid H_0)$. Computing a p-value and performing a frequentist test, at a minimum, does not require you to have any idea about $p(y mid H_1)$. Also, p-values are often integrals/sums of densities/pmfs, while a BF doesn't integrate over the data sample space.






          share|cite|improve this answer











          $endgroup$












          • $begingroup$
            Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
            $endgroup$
            – rnso
            1 hour ago



















          0












          $begingroup$

          The Bayes factor $B_01$ can be turned into a probability under equal weights as
          $$P_01=frac11+frac1large B_01$$but this does not make them comparable with a $p$-value since




          1. $P_01$ is a probability in the parameter space, not in the sampling space

          2. its value and range depend on the choice of the prior measure, they are thus relative rather than absolute

          3. both $B_01$ and $P_01$ contain a penalty for complexity (Occam's razor) by integrating out over the parameter space

          If you wish to consider a Bayesian equivalent to the $p$-value, the posterior predictive $p$-value (Meng, 1994) should be investigated
          $$Q_01=mathbb P(B_01(X)le B_01(x^textobs))$$
          where $x^textobs$ denotes the observation and $X$ is distributed from the posterior predictive
          $$Xsim int_Theta f(x|theta) pi(theta|x^textobs),textdtheta$$
          but this does not imply that the same "default" criteria for rejection and significance should apply to this object.






          share|cite|improve this answer









          $endgroup$













            Your Answer








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            2 Answers
            2






            active

            oldest

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            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            3












            $begingroup$

            A few things:



            The BF gives you evidence in favor of a hypothesis, while a frequentist hypothesis test gives you evidence against a (null) hypothesis. So it's kind of "apples to oranges."



            These two procedures, despite the difference in interpretations, may lead to different decisions. For example, a BF might reject while a frequentist hypothesis test doesn't, or vice versa. This problem is often referred to as the Jeffreys-Lindley's paradox. There have been many posts on this site about this; see e.g. here, and here.



            "At this P value, H1/H0 likelihood should be 95/5 or 19." No, this isn't true because, roughly $p(y mid H_1) neq 1- p(y mid H_0)$. Computing a p-value and performing a frequentist test, at a minimum, does not require you to have any idea about $p(y mid H_1)$. Also, p-values are often integrals/sums of densities/pmfs, while a BF doesn't integrate over the data sample space.






            share|cite|improve this answer











            $endgroup$












            • $begingroup$
              Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
              $endgroup$
              – rnso
              1 hour ago
















            3












            $begingroup$

            A few things:



            The BF gives you evidence in favor of a hypothesis, while a frequentist hypothesis test gives you evidence against a (null) hypothesis. So it's kind of "apples to oranges."



            These two procedures, despite the difference in interpretations, may lead to different decisions. For example, a BF might reject while a frequentist hypothesis test doesn't, or vice versa. This problem is often referred to as the Jeffreys-Lindley's paradox. There have been many posts on this site about this; see e.g. here, and here.



            "At this P value, H1/H0 likelihood should be 95/5 or 19." No, this isn't true because, roughly $p(y mid H_1) neq 1- p(y mid H_0)$. Computing a p-value and performing a frequentist test, at a minimum, does not require you to have any idea about $p(y mid H_1)$. Also, p-values are often integrals/sums of densities/pmfs, while a BF doesn't integrate over the data sample space.






            share|cite|improve this answer











            $endgroup$












            • $begingroup$
              Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
              $endgroup$
              – rnso
              1 hour ago














            3












            3








            3





            $begingroup$

            A few things:



            The BF gives you evidence in favor of a hypothesis, while a frequentist hypothesis test gives you evidence against a (null) hypothesis. So it's kind of "apples to oranges."



            These two procedures, despite the difference in interpretations, may lead to different decisions. For example, a BF might reject while a frequentist hypothesis test doesn't, or vice versa. This problem is often referred to as the Jeffreys-Lindley's paradox. There have been many posts on this site about this; see e.g. here, and here.



            "At this P value, H1/H0 likelihood should be 95/5 or 19." No, this isn't true because, roughly $p(y mid H_1) neq 1- p(y mid H_0)$. Computing a p-value and performing a frequentist test, at a minimum, does not require you to have any idea about $p(y mid H_1)$. Also, p-values are often integrals/sums of densities/pmfs, while a BF doesn't integrate over the data sample space.






            share|cite|improve this answer











            $endgroup$



            A few things:



            The BF gives you evidence in favor of a hypothesis, while a frequentist hypothesis test gives you evidence against a (null) hypothesis. So it's kind of "apples to oranges."



            These two procedures, despite the difference in interpretations, may lead to different decisions. For example, a BF might reject while a frequentist hypothesis test doesn't, or vice versa. This problem is often referred to as the Jeffreys-Lindley's paradox. There have been many posts on this site about this; see e.g. here, and here.



            "At this P value, H1/H0 likelihood should be 95/5 or 19." No, this isn't true because, roughly $p(y mid H_1) neq 1- p(y mid H_0)$. Computing a p-value and performing a frequentist test, at a minimum, does not require you to have any idea about $p(y mid H_1)$. Also, p-values are often integrals/sums of densities/pmfs, while a BF doesn't integrate over the data sample space.







            share|cite|improve this answer














            share|cite|improve this answer



            share|cite|improve this answer








            edited 21 mins ago









            Xi'an

            59.8k897369




            59.8k897369










            answered 2 hours ago









            TaylorTaylor

            12.7k21946




            12.7k21946











            • $begingroup$
              Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
              $endgroup$
              – rnso
              1 hour ago

















            • $begingroup$
              Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
              $endgroup$
              – rnso
              1 hour ago
















            $begingroup$
            Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
            $endgroup$
            – rnso
            1 hour ago





            $begingroup$
            Thanks for your insight. However, if evidence in favor of a hypothesis is apple, I think evidence for alternate hypothesis can be inverted apple but not orange! Also, what would you say is approximate Bayes Factor value corresponding to P=0.05?
            $endgroup$
            – rnso
            1 hour ago














            0












            $begingroup$

            The Bayes factor $B_01$ can be turned into a probability under equal weights as
            $$P_01=frac11+frac1large B_01$$but this does not make them comparable with a $p$-value since




            1. $P_01$ is a probability in the parameter space, not in the sampling space

            2. its value and range depend on the choice of the prior measure, they are thus relative rather than absolute

            3. both $B_01$ and $P_01$ contain a penalty for complexity (Occam's razor) by integrating out over the parameter space

            If you wish to consider a Bayesian equivalent to the $p$-value, the posterior predictive $p$-value (Meng, 1994) should be investigated
            $$Q_01=mathbb P(B_01(X)le B_01(x^textobs))$$
            where $x^textobs$ denotes the observation and $X$ is distributed from the posterior predictive
            $$Xsim int_Theta f(x|theta) pi(theta|x^textobs),textdtheta$$
            but this does not imply that the same "default" criteria for rejection and significance should apply to this object.






            share|cite|improve this answer









            $endgroup$

















              0












              $begingroup$

              The Bayes factor $B_01$ can be turned into a probability under equal weights as
              $$P_01=frac11+frac1large B_01$$but this does not make them comparable with a $p$-value since




              1. $P_01$ is a probability in the parameter space, not in the sampling space

              2. its value and range depend on the choice of the prior measure, they are thus relative rather than absolute

              3. both $B_01$ and $P_01$ contain a penalty for complexity (Occam's razor) by integrating out over the parameter space

              If you wish to consider a Bayesian equivalent to the $p$-value, the posterior predictive $p$-value (Meng, 1994) should be investigated
              $$Q_01=mathbb P(B_01(X)le B_01(x^textobs))$$
              where $x^textobs$ denotes the observation and $X$ is distributed from the posterior predictive
              $$Xsim int_Theta f(x|theta) pi(theta|x^textobs),textdtheta$$
              but this does not imply that the same "default" criteria for rejection and significance should apply to this object.






              share|cite|improve this answer









              $endgroup$















                0












                0








                0





                $begingroup$

                The Bayes factor $B_01$ can be turned into a probability under equal weights as
                $$P_01=frac11+frac1large B_01$$but this does not make them comparable with a $p$-value since




                1. $P_01$ is a probability in the parameter space, not in the sampling space

                2. its value and range depend on the choice of the prior measure, they are thus relative rather than absolute

                3. both $B_01$ and $P_01$ contain a penalty for complexity (Occam's razor) by integrating out over the parameter space

                If you wish to consider a Bayesian equivalent to the $p$-value, the posterior predictive $p$-value (Meng, 1994) should be investigated
                $$Q_01=mathbb P(B_01(X)le B_01(x^textobs))$$
                where $x^textobs$ denotes the observation and $X$ is distributed from the posterior predictive
                $$Xsim int_Theta f(x|theta) pi(theta|x^textobs),textdtheta$$
                but this does not imply that the same "default" criteria for rejection and significance should apply to this object.






                share|cite|improve this answer









                $endgroup$



                The Bayes factor $B_01$ can be turned into a probability under equal weights as
                $$P_01=frac11+frac1large B_01$$but this does not make them comparable with a $p$-value since




                1. $P_01$ is a probability in the parameter space, not in the sampling space

                2. its value and range depend on the choice of the prior measure, they are thus relative rather than absolute

                3. both $B_01$ and $P_01$ contain a penalty for complexity (Occam's razor) by integrating out over the parameter space

                If you wish to consider a Bayesian equivalent to the $p$-value, the posterior predictive $p$-value (Meng, 1994) should be investigated
                $$Q_01=mathbb P(B_01(X)le B_01(x^textobs))$$
                where $x^textobs$ denotes the observation and $X$ is distributed from the posterior predictive
                $$Xsim int_Theta f(x|theta) pi(theta|x^textobs),textdtheta$$
                but this does not imply that the same "default" criteria for rejection and significance should apply to this object.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered 10 mins ago









                Xi'anXi'an

                59.8k897369




                59.8k897369



























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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result