I was analysing a distribution. I have attached the link for the list.
https://drive.google.com/file/d/1o1Zr9bwy_wzrDAIdsxRVXxAPzRrblQOg/view?usp=sharing
This histogram of this distribution looks like this,
Now I was evaluating the skewness of the distribution. First I used the basic formula of skewness.
[ I don't know name of the formula. If someone can enlighten me that's a plus. :) ]
Code:
meanY = np.mean(yArr)
stdY = np.std(yArr)
s = 0
for yd in yArr:
s += (yd-meanY)**3
print((s/(stdY**3))/len(yArr))
Output:
-0.6510082464944021
Then I used Pearson's formula for skewness ie.
Code:
meanY = np.mean(yArr)
medianY = np.median(yArr)
stdY = np.std(yArr)
print(3*(meanY-medianY)/stdY)
Output:
0.34088557298815947
Now the first formula is saying the graph is right-skewed but the second formula is saying the graph is left-skewed. Why there is a conflict between the results of the two formulas?
Overall, I want to know why there is a difference in the report of both the formulas and the general conditions where Pearson's skewness formula will come at conflict with the traditional formula.