Descriptive statistical terms
Mean, median, mode, variance, and standard deviation are popularly called measures of centrality and spread. It has to be the first step in any statistical data analysis.
Skewness and kurtosis are two tests that test for normality. If Skwness is 0 and kurtosis is 3, then it signifies that the distribution of data is perfectly normal (symmetric). If we conclude the distribution to be normal, we can infer a lot of other parameters about the distribution.
If the value of skewness is negative, then the data is left-skewed and if the value is positive then the data is right-skewed. Similarly, positive kurtosis indicates heavy-tailed distribution and vice versa
Python3
import numpy as npfrom scipy.stats import describev = np.random.normal(size=100)result = describe(v)print(result) |
Output:
SciPy – Statistical Significance Tests
In probability and statistics, we tend to compute a lot of different numbers, the job doesn’t end there, it is highly essential to statistically interpret the numbers to understand the significance of that number to the particular problem at hand. In statistics, there are few techniques to assess the significance of these numbers. In the article let us discuss a few of the Statistical Significance tests supported by the Python package.
Some of the prominent and widely used statistical significance tests are as follows,’
- Hypothesis test – One-tailed test and Two-tailed test
- T-test
- Kolmogorov Smirnoff test