Statistics

Mean : sensitive to outliers

np.mean()
np.nammean()

Median : Not sensitive to outliers

np.median()

when a distribution is symmetrical, the mean and the median value coincide.

Mode : The most frequently occurring value in a distribution.

Python Code :

from scipy import stats.
data = [1, 3, 4, 4, 7]
stats.mode(data)
(array([4]), array([ 2.]))

Geometric Mean : Can be useful to describe the location of a distribution

Python Code :

x = np.arange(1,101)
stats.gmean(x)
37.992689344834304

Range : It is simply the difference between the highest and the lowest data value.

Python Code :

range = np.ptp(x) # peak to peak

Percentiles :

The simplest way to understand centiles, also called percentiles, is to first define the Cumulative Distribution Function (CDF):

The CDF is the integral of the PDF from minus infinity up to the given value, and thus specifies the percentage of the data that lie below this value.

Standard Deviation and Variance :

Python Code :

data = np.arange(7,14)
np.std(data, ddof=0)
2.0
np.std(data, ddof=1)
2.16025

Chi Squared test :

The chi-squared test is used to determine whether there is a significant difference between the expected frequencies and the observed frequencies in one or more categories.

A/B testing:

Randomized experiment with two variants, A and B. It includes application of statistical hypothesis testing or “two-sample hypothesis testing” as used in the field of statistics. A/B testing is a way to compare two versions of a single variable, typically by testing a subject’s response to variant A against variant B, and determining which of the two variants is more effective.