5 duplicates list edited from If my histogram shows a bell-shaped curve, can I say my data is normally distributed? to Appropriate normality tests for small samples, Is normality testing 'essentially useless'?, Testing normality, What tests do I use to confirm that residuals are normally distributed?, If my histogram shows a bell-shaped curve, can I say my data is normally distributed?
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Why according to distribution graph it'ssit's normally distributed, but Jarque-Bera test shows non-normally distributed?

I am doing exerciseworking on Kaggle House PrisingKaggle's house pricing exercise and I cannot understand something. I I watch and read articles for Normality teston normality tests, and more specifically JB test, but I cannot understand why according to my understanding of that test iI need to reject the Null H. whichnull hypothesis (which is Normal Distributionthe normal distribution) and conclude it is not normala non-normal distribution when the distribution graph shows a very close result to Normala normal distribution?

Jarque-Bera test = 171.236, with p-value 6.55459e-038

Jarque-Bera test = 171.236, with p-value 6.55459e-038 SoSo from that result, if I am correct, I reject null h and conclude the data isare not normally distributed.

  But then this is the Distribution Grph.distribution graph (n=1460)  :

enter image description here

PS. The Y var is log of price and the x is Yearyear. Could the problem be that year is not a continuous variable?

Why according to distribution graph it'ss normally distributed, but Jarque-Bera test shows non-normally distributed?

I am doing exercise on Kaggle House Prising and I cannot understand something. I watch and read articles for Normality test and more specifically JB test but I cannot understand why according to my understanding of that test i need to reject the Null H. which is Normal Distribution and conclude it is not normal distribution when the distribution graph shows very close result to Normal distribution?

Jarque-Bera test = 171.236, with p-value 6.55459e-038 So from that result if I am correct, I reject null h and conclude the data is not normally distributed.

  But then this is the Distribution Grph. (n=1460)  enter image description here

PS. The Y var is log of price and the x is Year. Could the problem be that year is not continuous variable?

Why according to distribution graph it's normally distributed, but Jarque-Bera test shows non-normally distributed?

I am working on Kaggle's house pricing exercise and I cannot understand something. I watch and read articles on normality tests, and more specifically JB test, but I cannot understand why according to my understanding of that test I need to reject the null hypothesis (which is the normal distribution) and conclude it is a non-normal distribution when the distribution graph shows a very close result to a normal distribution?

Jarque-Bera test = 171.236, with p-value 6.55459e-038

So from that result, if I am correct, I reject null and conclude the data are not normally distributed. But then this is the distribution graph (n=1460):

enter image description here

PS. The Y var is log of price and the x is year. Could the problem be that year is not a continuous variable?

3 Fix grammar and spelling
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why Why according to ditributiondistribution graph its normalit'ss normally distributed, but Jarque-Bera test shows Non normallynon-normally distributed?

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