Testing strongly binned data for normality?

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  • Thread starter Thread starter Gerenuk
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SUMMARY

The discussion centers on testing the normality of strongly binned data, specifically using the provided dataset that exhibits a sharp drop in frequency. The user seeks a method to calculate the probability that the data is normally distributed, as deviations could indicate data manipulation. The reference to frequentist tests for normality suggests a focus on statistical methods such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test to assess the distribution's conformity to normality.

PREREQUISITES
  • Understanding of statistical concepts such as normal distribution and hypothesis testing.
  • Familiarity with normality tests, specifically the Shapiro-Wilk test and Kolmogorov-Smirnov test.
  • Experience with data visualization techniques to interpret binned data distributions.
  • Proficiency in statistical software or programming languages like R or Python for data analysis.
NEXT STEPS
  • Research the Shapiro-Wilk test and its application in testing for normality.
  • Learn about the Kolmogorov-Smirnov test and how it compares distributions.
  • Explore data visualization techniques for binned data to identify distribution patterns.
  • Investigate methods for detecting data manipulation and fraud in statistical datasets.
USEFUL FOR

Data analysts, statisticians, and researchers involved in data integrity verification and normality testing of binned datasets.

Gerenuk
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Hi,
if I have some spread over a very small range of binned values, how can I test if the distribution is normal. Basically if it weren't, then the data would be fraud data. Is there a way to calculate something like a probability that the data was not manipulated and is correctly given by the normal distribution?
My data looks like
-3 -2 -1 0 1 2 3
0 1 150 200 150 1 0
I mean there is a too sharp drop and I'm searching for a probability that the data isn't normal.
 
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