QQ Plot and Shapiro Wilk Test Disagree












1












$begingroup$


My QQ Plot shows that the data is not normally distributed



qqplot(residual_values, fit = True, line = '45')
pylab.show()


enter image description here



It has a skewness of 0.54



residual_values.skew()  # 0.5469389365591185


But the p_value of Shapiro test is greater than 0.05, telling me that it is normally distributed



shapiro(residual_values) # (0.9569438099861145, 0.2261517345905304)


What is the correct inference from this, Is it Normally Distributed or not?










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  • 3




    $begingroup$
    The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
    $endgroup$
    – The Laconic
    5 hours ago






  • 3




    $begingroup$
    It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
    $endgroup$
    – Nick Cox
    5 hours ago










  • $begingroup$
    @TheLaconic Sorry I am new to ML.
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    @NickCox Thank you Sorry reputation is low to upvote you guys
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
    $endgroup$
    – Glen_b
    3 hours ago


















1












$begingroup$


My QQ Plot shows that the data is not normally distributed



qqplot(residual_values, fit = True, line = '45')
pylab.show()


enter image description here



It has a skewness of 0.54



residual_values.skew()  # 0.5469389365591185


But the p_value of Shapiro test is greater than 0.05, telling me that it is normally distributed



shapiro(residual_values) # (0.9569438099861145, 0.2261517345905304)


What is the correct inference from this, Is it Normally Distributed or not?










share|cite|improve this question







New contributor




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







$endgroup$








  • 3




    $begingroup$
    The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
    $endgroup$
    – The Laconic
    5 hours ago






  • 3




    $begingroup$
    It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
    $endgroup$
    – Nick Cox
    5 hours ago










  • $begingroup$
    @TheLaconic Sorry I am new to ML.
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    @NickCox Thank you Sorry reputation is low to upvote you guys
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
    $endgroup$
    – Glen_b
    3 hours ago
















1












1








1





$begingroup$


My QQ Plot shows that the data is not normally distributed



qqplot(residual_values, fit = True, line = '45')
pylab.show()


enter image description here



It has a skewness of 0.54



residual_values.skew()  # 0.5469389365591185


But the p_value of Shapiro test is greater than 0.05, telling me that it is normally distributed



shapiro(residual_values) # (0.9569438099861145, 0.2261517345905304)


What is the correct inference from this, Is it Normally Distributed or not?










share|cite|improve this question







New contributor




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







$endgroup$




My QQ Plot shows that the data is not normally distributed



qqplot(residual_values, fit = True, line = '45')
pylab.show()


enter image description here



It has a skewness of 0.54



residual_values.skew()  # 0.5469389365591185


But the p_value of Shapiro test is greater than 0.05, telling me that it is normally distributed



shapiro(residual_values) # (0.9569438099861145, 0.2261517345905304)


What is the correct inference from this, Is it Normally Distributed or not?







regression machine-learning






share|cite|improve this question







New contributor




Shinigami 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




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Check out our Code of Conduct.









share|cite|improve this question




share|cite|improve this question






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asked 7 hours ago









ShinigamiShinigami

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New contributor





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








  • 3




    $begingroup$
    The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
    $endgroup$
    – The Laconic
    5 hours ago






  • 3




    $begingroup$
    It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
    $endgroup$
    – Nick Cox
    5 hours ago










  • $begingroup$
    @TheLaconic Sorry I am new to ML.
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    @NickCox Thank you Sorry reputation is low to upvote you guys
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
    $endgroup$
    – Glen_b
    3 hours ago
















  • 3




    $begingroup$
    The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
    $endgroup$
    – The Laconic
    5 hours ago






  • 3




    $begingroup$
    It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
    $endgroup$
    – Nick Cox
    5 hours ago










  • $begingroup$
    @TheLaconic Sorry I am new to ML.
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    @NickCox Thank you Sorry reputation is low to upvote you guys
    $endgroup$
    – Shinigami
    4 hours ago










  • $begingroup$
    It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
    $endgroup$
    – Glen_b
    3 hours ago










3




3




$begingroup$
The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
$endgroup$
– The Laconic
5 hours ago




$begingroup$
The QQ plot looks consistent with being normally distributed. Did you expect every point to fall exactly on the line?
$endgroup$
– The Laconic
5 hours ago




3




3




$begingroup$
It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
$endgroup$
– Nick Cox
5 hours ago




$begingroup$
It is approximately normally distributed if you are prepared to discount slight skewness. No procedure ever indicates more.
$endgroup$
– Nick Cox
5 hours ago












$begingroup$
@TheLaconic Sorry I am new to ML.
$endgroup$
– Shinigami
4 hours ago




$begingroup$
@TheLaconic Sorry I am new to ML.
$endgroup$
– Shinigami
4 hours ago












$begingroup$
@NickCox Thank you Sorry reputation is low to upvote you guys
$endgroup$
– Shinigami
4 hours ago




$begingroup$
@NickCox Thank you Sorry reputation is low to upvote you guys
$endgroup$
– Shinigami
4 hours ago












$begingroup$
It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
$endgroup$
– Glen_b
3 hours ago






$begingroup$
It's approximately normal, the skewness in the sample is quite mild; this doesn't automatically mean the population is also skewed (though I expect it is). A high p-value on a test of normality doesn't mean that it is normal, only that you couldn't detect whatever population non-normality there was. (The answer to "is it normally distributed" is "no" - unless you generated it to be normal it won't actually be normal -- but why would it have to be?)
$endgroup$
– Glen_b
3 hours ago












4 Answers
4






active

oldest

votes


















1












$begingroup$

The q-q is consistent with (not "proving") approximate normality, more or less.



The Shapiro-Wilk is a formal test of normality and as such, it cannot confirm the null hypothesis of normality. The data may be reasonably consistent with normality yet still be from a different nonnormal underlying distribution. Frequentist hypothesis tests, as a general rule, cannot prove a hypothesis, and failure to reject (p>alpha) does not support the null hypothesis.



@The Laconic gave some decent advice to interpret the q-q plot. However, large p-values do not lead you to accept the null hypothesis (therefore, you don't conclude normality based on this test; the best you can do is say insufficient evidence of nonnormality at the a priori chosen alpha level).






share|cite|improve this answer









$endgroup$





















    1












    $begingroup$

    The shapiro-wilk p-value being >0.05 indicates lack of evidence to against normality. That is consistent with the QQ plot you showed, which is not too far off the line. I don't see what the inconsistency is here. Also, you should give a CI for the skewness coefficient.






    share|cite|improve this answer









    $endgroup$





















      0












      $begingroup$

      The QQ plot is an informal test of normality that can give you some insight into the nature of deviations from normality; for example, whether the distribution has some skew, or fat tails, or whether there are specific observations that deviate from what you would expect from a normal distribution (outliers). The QQ plot can often convince you that the distribution is definitely not normal, but this isn't such a case. Here, the points fall more or less along the line, which is broadly consistent with normality--intuitively, the sort of variation you would expect to see in a small sample.



      The Shapiro test is a formal test of normality. I'm not familiar with the shapiro function's output, so I'm not sure which number, if either, is supposed to be the p-value, but if you say it's largish, then we are led to accept the null hypothesis of normality. And this is consistent with what we see qualitatively in the QQ plot.






      share|cite|improve this answer









      $endgroup$





















        0












        $begingroup$

        My understanding is that, given power issues with normality tests, they are not highly recommended. As a result I don't use them any more, preferring QQ plots (which are recommended in the literature I have seen).






        share|cite|improve this answer









        $endgroup$













        • $begingroup$
          I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
          $endgroup$
          – LSC
          1 hour ago













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        4 Answers
        4






        active

        oldest

        votes








        4 Answers
        4






        active

        oldest

        votes









        active

        oldest

        votes






        active

        oldest

        votes









        1












        $begingroup$

        The q-q is consistent with (not "proving") approximate normality, more or less.



        The Shapiro-Wilk is a formal test of normality and as such, it cannot confirm the null hypothesis of normality. The data may be reasonably consistent with normality yet still be from a different nonnormal underlying distribution. Frequentist hypothesis tests, as a general rule, cannot prove a hypothesis, and failure to reject (p>alpha) does not support the null hypothesis.



        @The Laconic gave some decent advice to interpret the q-q plot. However, large p-values do not lead you to accept the null hypothesis (therefore, you don't conclude normality based on this test; the best you can do is say insufficient evidence of nonnormality at the a priori chosen alpha level).






        share|cite|improve this answer









        $endgroup$


















          1












          $begingroup$

          The q-q is consistent with (not "proving") approximate normality, more or less.



          The Shapiro-Wilk is a formal test of normality and as such, it cannot confirm the null hypothesis of normality. The data may be reasonably consistent with normality yet still be from a different nonnormal underlying distribution. Frequentist hypothesis tests, as a general rule, cannot prove a hypothesis, and failure to reject (p>alpha) does not support the null hypothesis.



          @The Laconic gave some decent advice to interpret the q-q plot. However, large p-values do not lead you to accept the null hypothesis (therefore, you don't conclude normality based on this test; the best you can do is say insufficient evidence of nonnormality at the a priori chosen alpha level).






          share|cite|improve this answer









          $endgroup$
















            1












            1








            1





            $begingroup$

            The q-q is consistent with (not "proving") approximate normality, more or less.



            The Shapiro-Wilk is a formal test of normality and as such, it cannot confirm the null hypothesis of normality. The data may be reasonably consistent with normality yet still be from a different nonnormal underlying distribution. Frequentist hypothesis tests, as a general rule, cannot prove a hypothesis, and failure to reject (p>alpha) does not support the null hypothesis.



            @The Laconic gave some decent advice to interpret the q-q plot. However, large p-values do not lead you to accept the null hypothesis (therefore, you don't conclude normality based on this test; the best you can do is say insufficient evidence of nonnormality at the a priori chosen alpha level).






            share|cite|improve this answer









            $endgroup$



            The q-q is consistent with (not "proving") approximate normality, more or less.



            The Shapiro-Wilk is a formal test of normality and as such, it cannot confirm the null hypothesis of normality. The data may be reasonably consistent with normality yet still be from a different nonnormal underlying distribution. Frequentist hypothesis tests, as a general rule, cannot prove a hypothesis, and failure to reject (p>alpha) does not support the null hypothesis.



            @The Laconic gave some decent advice to interpret the q-q plot. However, large p-values do not lead you to accept the null hypothesis (therefore, you don't conclude normality based on this test; the best you can do is say insufficient evidence of nonnormality at the a priori chosen alpha level).







            share|cite|improve this answer












            share|cite|improve this answer



            share|cite|improve this answer










            answered 3 hours ago









            LSCLSC

            1697




            1697

























                1












                $begingroup$

                The shapiro-wilk p-value being >0.05 indicates lack of evidence to against normality. That is consistent with the QQ plot you showed, which is not too far off the line. I don't see what the inconsistency is here. Also, you should give a CI for the skewness coefficient.






                share|cite|improve this answer









                $endgroup$


















                  1












                  $begingroup$

                  The shapiro-wilk p-value being >0.05 indicates lack of evidence to against normality. That is consistent with the QQ plot you showed, which is not too far off the line. I don't see what the inconsistency is here. Also, you should give a CI for the skewness coefficient.






                  share|cite|improve this answer









                  $endgroup$
















                    1












                    1








                    1





                    $begingroup$

                    The shapiro-wilk p-value being >0.05 indicates lack of evidence to against normality. That is consistent with the QQ plot you showed, which is not too far off the line. I don't see what the inconsistency is here. Also, you should give a CI for the skewness coefficient.






                    share|cite|improve this answer









                    $endgroup$



                    The shapiro-wilk p-value being >0.05 indicates lack of evidence to against normality. That is consistent with the QQ plot you showed, which is not too far off the line. I don't see what the inconsistency is here. Also, you should give a CI for the skewness coefficient.







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered 3 hours ago









                    beta1_equals_beta2beta1_equals_beta2

                    512




                    512























                        0












                        $begingroup$

                        The QQ plot is an informal test of normality that can give you some insight into the nature of deviations from normality; for example, whether the distribution has some skew, or fat tails, or whether there are specific observations that deviate from what you would expect from a normal distribution (outliers). The QQ plot can often convince you that the distribution is definitely not normal, but this isn't such a case. Here, the points fall more or less along the line, which is broadly consistent with normality--intuitively, the sort of variation you would expect to see in a small sample.



                        The Shapiro test is a formal test of normality. I'm not familiar with the shapiro function's output, so I'm not sure which number, if either, is supposed to be the p-value, but if you say it's largish, then we are led to accept the null hypothesis of normality. And this is consistent with what we see qualitatively in the QQ plot.






                        share|cite|improve this answer









                        $endgroup$


















                          0












                          $begingroup$

                          The QQ plot is an informal test of normality that can give you some insight into the nature of deviations from normality; for example, whether the distribution has some skew, or fat tails, or whether there are specific observations that deviate from what you would expect from a normal distribution (outliers). The QQ plot can often convince you that the distribution is definitely not normal, but this isn't such a case. Here, the points fall more or less along the line, which is broadly consistent with normality--intuitively, the sort of variation you would expect to see in a small sample.



                          The Shapiro test is a formal test of normality. I'm not familiar with the shapiro function's output, so I'm not sure which number, if either, is supposed to be the p-value, but if you say it's largish, then we are led to accept the null hypothesis of normality. And this is consistent with what we see qualitatively in the QQ plot.






                          share|cite|improve this answer









                          $endgroup$
















                            0












                            0








                            0





                            $begingroup$

                            The QQ plot is an informal test of normality that can give you some insight into the nature of deviations from normality; for example, whether the distribution has some skew, or fat tails, or whether there are specific observations that deviate from what you would expect from a normal distribution (outliers). The QQ plot can often convince you that the distribution is definitely not normal, but this isn't such a case. Here, the points fall more or less along the line, which is broadly consistent with normality--intuitively, the sort of variation you would expect to see in a small sample.



                            The Shapiro test is a formal test of normality. I'm not familiar with the shapiro function's output, so I'm not sure which number, if either, is supposed to be the p-value, but if you say it's largish, then we are led to accept the null hypothesis of normality. And this is consistent with what we see qualitatively in the QQ plot.






                            share|cite|improve this answer









                            $endgroup$



                            The QQ plot is an informal test of normality that can give you some insight into the nature of deviations from normality; for example, whether the distribution has some skew, or fat tails, or whether there are specific observations that deviate from what you would expect from a normal distribution (outliers). The QQ plot can often convince you that the distribution is definitely not normal, but this isn't such a case. Here, the points fall more or less along the line, which is broadly consistent with normality--intuitively, the sort of variation you would expect to see in a small sample.



                            The Shapiro test is a formal test of normality. I'm not familiar with the shapiro function's output, so I'm not sure which number, if either, is supposed to be the p-value, but if you say it's largish, then we are led to accept the null hypothesis of normality. And this is consistent with what we see qualitatively in the QQ plot.







                            share|cite|improve this answer












                            share|cite|improve this answer



                            share|cite|improve this answer










                            answered 4 hours ago









                            The LaconicThe Laconic

                            1,0721615




                            1,0721615























                                0












                                $begingroup$

                                My understanding is that, given power issues with normality tests, they are not highly recommended. As a result I don't use them any more, preferring QQ plots (which are recommended in the literature I have seen).






                                share|cite|improve this answer









                                $endgroup$













                                • $begingroup$
                                  I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                  $endgroup$
                                  – LSC
                                  1 hour ago


















                                0












                                $begingroup$

                                My understanding is that, given power issues with normality tests, they are not highly recommended. As a result I don't use them any more, preferring QQ plots (which are recommended in the literature I have seen).






                                share|cite|improve this answer









                                $endgroup$













                                • $begingroup$
                                  I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                  $endgroup$
                                  – LSC
                                  1 hour ago
















                                0












                                0








                                0





                                $begingroup$

                                My understanding is that, given power issues with normality tests, they are not highly recommended. As a result I don't use them any more, preferring QQ plots (which are recommended in the literature I have seen).






                                share|cite|improve this answer









                                $endgroup$



                                My understanding is that, given power issues with normality tests, they are not highly recommended. As a result I don't use them any more, preferring QQ plots (which are recommended in the literature I have seen).







                                share|cite|improve this answer












                                share|cite|improve this answer



                                share|cite|improve this answer










                                answered 3 hours ago









                                user54285user54285

                                413




                                413












                                • $begingroup$
                                  I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                  $endgroup$
                                  – LSC
                                  1 hour ago




















                                • $begingroup$
                                  I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                  $endgroup$
                                  – LSC
                                  1 hour ago


















                                $begingroup$
                                I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                $endgroup$
                                – LSC
                                1 hour ago






                                $begingroup$
                                I was under the impression formal tests of normality are usually too powerful and too frequently detect immaterial departures from normality. Visualization is generally preferred, as you said (and theoretical knowledge when available).
                                $endgroup$
                                – LSC
                                1 hour ago












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