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Type 1 Error & Type 2 Error's pregnancy test analogy: is it legit?


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2












$begingroup$


enter image description here



I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.



As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.



So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?



For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.



However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.



I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)



But based on the table below, that doesn't seem to make any sense.



So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?



enter image description here










share|improve this question







New contributor




user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$

















    2












    $begingroup$


    enter image description here



    I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.



    As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.



    So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?



    For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.



    However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.



    I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)



    But based on the table below, that doesn't seem to make any sense.



    So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?



    enter image description here










    share|improve this question







    New contributor




    user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      2












      2








      2





      $begingroup$


      enter image description here



      I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.



      As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.



      So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?



      For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.



      However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.



      I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)



      But based on the table below, that doesn't seem to make any sense.



      So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?



      enter image description here










      share|improve this question







      New contributor




      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      enter image description here



      I found this picture in my stats book but I'm now confused to what 'positive' and 'negative' is referring to.



      As seen in the table below, Type 1 error is the error that its H0 is actually true but FALSEly claims that it's false. Type 2 error, on the other hand, is the error that its H0 is actually false but FALSEly claims that it's true.



      So my question is, how do the pregnancy analogy and whole 'false positive' & 'false negative' thing make sense?



      For the first picture to be a type 1 error, H0 should be "The person is NOT pregnant" so that "You're pregnant" statement becomes false.



      However, the second picture has the complete opposite H0, where H0 should be "The person is pregnant" so that "You're not pregnant" statement becomes false.



      I thought it was really confusing because I thought false POSITIVE and false NEGATIVE corresponded to "You're pregnant"(positive) / "You're NOT pregnant"(negative)



      But based on the table below, that doesn't seem to make any sense.



      So the question is, is there anything that I'm missing here or is it just that textbook's analogy sucks?



      enter image description here







      statistics






      share|improve this question







      New contributor




      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 11 hours ago









      user8491363user8491363

      132




      132




      New contributor




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      New contributor





      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      user8491363 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






















          1 Answer
          1






          active

          oldest

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          3












          $begingroup$

          Presumably here




          • the null hypothesis is $H_0:$ You are not pregnant

          • the alternative hypothesis is $H_1:$ You are pregnant


          so being pregnant would be the positive result.



          You take a pregnancy test




          • if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test


          • if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test



          So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation






          share|improve this answer









          $endgroup$














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            1 Answer
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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

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            active

            oldest

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            3












            $begingroup$

            Presumably here




            • the null hypothesis is $H_0:$ You are not pregnant

            • the alternative hypothesis is $H_1:$ You are pregnant


            so being pregnant would be the positive result.



            You take a pregnancy test




            • if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test


            • if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test



            So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation






            share|improve this answer









            $endgroup$


















              3












              $begingroup$

              Presumably here




              • the null hypothesis is $H_0:$ You are not pregnant

              • the alternative hypothesis is $H_1:$ You are pregnant


              so being pregnant would be the positive result.



              You take a pregnancy test




              • if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test


              • if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test



              So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation






              share|improve this answer









              $endgroup$
















                3












                3








                3





                $begingroup$

                Presumably here




                • the null hypothesis is $H_0:$ You are not pregnant

                • the alternative hypothesis is $H_1:$ You are pregnant


                so being pregnant would be the positive result.



                You take a pregnancy test




                • if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test


                • if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test



                So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation






                share|improve this answer









                $endgroup$



                Presumably here




                • the null hypothesis is $H_0:$ You are not pregnant

                • the alternative hypothesis is $H_1:$ You are pregnant


                so being pregnant would be the positive result.



                You take a pregnancy test




                • if the pregnancy test gives a positive result when you are not pregnant then this is a false positive, a Type I error when the null hypothesis $H_0$ is in fact true but has been rejected by the test


                • if the pregnancy test gives a negative result when you are pregnant then this is a false negative, a Type II error when the null hypothesis $H_0$ is in fact untrue but has not been rejected by the test



                So in a statement of being a true/false positive/negative test, the true/false part is about the accuracy of the test while the positive/negative part is about the result of the test rather than being the real situation







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 10 hours ago









                HenryHenry

                3,826316




                3,826316






















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