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These results were generate by Normal distribution on September 22, 2023 at 2:42 PM (UTC).
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| Data set name | Code Correctness Copilot | |
|---|---|---|
| Data set notes | Python | |
| Sample size | N = 600 | |
| Distribution parameters | The mean = 0.375 and standard deviation = 0.485. |
| Appears normal | ||
|---|---|---|
| Skewness | Population skewness (unbiased) = 0.518, with a standard error of 0.100. The (asymptotic) z-value is 4.939 and p-value < 0.001. Therefore, this distribution does not have similar skew as a normal distribution. | |
| Kurtosis | Population excess kurtosis (unbiased) = -1.738, with a standard error of 0.199. The (asymptotic) z-value is 54.888 and p-value < 0.001. Therefore, this distribution does not have similar kurtosis as a normal distribution. | |
| Kolmogorov-Smirnov | The two-sided Kolmogorov-Smirnov statistic (the maximum absolute difference) D(600) = 0.406, Lilliefors p-value < 0.001. Since the p-value is less than 0.05, the data does not appear to be normally distributed. | |
| Shapiro–Wilk | The Shapiro–Wilk test statistic W = 0.613, p-value < 0.001. Since the p-value is less than 0.05, the data does not appear to be normally distributed. |
The above graph shows both a histogram of the data and the most likely normal distribution
for the data. If histogram and normal distribution have a similar shape, it is likely
that the underlying population is approximately normal.
In the graph above, the red points represent the numbers in your data set. If these points
are located relatively close to the black line, your data is approximately normally
distributed.