B Question about Chi-Square Test Regarding Normal Distribution

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Grouping data differently can lead to varying conclusions in a Chi-Square test, as demonstrated by the example where one grouping accepted the null hypothesis while another rejected it. The sensitivity of the test to bin size and degrees of freedom plays a crucial role in these outcomes. With a sample size of only 50, the results may not reliably confirm a normal distribution, suggesting the use of the Shapiro-Wilk test for better assessment of normality. The Central Limit Theorem can be applied to assume normality despite the small sample size. Different groupings can provide more detail and affect the statistical analysis significantly.
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Let say I have 50 raw data of height of students. I want to do goodness of fit test to check whether normal distribution is appropriate model for the data at a certain significance level
The first step is to group the data and make a table so I can get the observed frequency for each data interval. I did two different groupings (something like 150 - 160 , 160 - 170 , etc and the other is 150 - 170, 170 - 190, etc) and found out that the conclusion of the hypothesis is different, one resulting in accepting null hypothesis and the other rejecting the null hypothesis.

Is it possible different grouping resulting in different conclusion? Or there should be mistake in my working?

Thanks
 
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You suffer from low statistics -- 50 events isn't much to confirm a distribution.
 
In reality, as everyone knows the height of individuals has finite variance, you can just rely on the CLT with n=50 to assume normality
 
It certainly is possible to get different results. Your first grouping would show more detail than your second grouping. It would also have twice the degrees of freedom, so the Chi-Squared distribution is different.
 
Thank you very much for the help and explanation BWV, BvU, FactChecker
 
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