How to Conduct a One Sample T-Test in Python

In this article, we are going to see how to conduct a one sample T-Test in Python.

One Sample T-Test in Python

The one-sample t-test is a statistical hypothesis test that can be used to see if the mean of an unknown population differs from a given or known value. In this article let’s learn how to perform a one-sample t-test.

null hypothesis: the mean of the areas is 5000.

alternative hypothesis: the mean of the areas is not  5000.

CSV Used:

Create a Dataframe for demonestration 

Python3

# import packages
import scipy.stats as stats
import pandas as pd

# loading the csv file
data = pd.read_csv('areas.csv')
data.head()

Output:

Conduct a One Sample T-Test in Python

To perform one-sample t-test we will use the scipy.stats.ttest_1samp() function to perform one- sample t-test. The T-test is calculated for the mean of one set of values. The null hypothesis is that the expected mean of a sample of independent observations is equal to the specified population mean, popmean.

Syntax: scipy.stats.ttest_1samp(a, popmean, axis=0).

parameters:

• a : an array or iterable object of sample observations.
• popmean : expected mean in the null hypothesis.
• axis : its an optional parameter. represents axis.

returns : t statistic and two tailed p value.

Python3

# import packages
import scipy.stats as stats
import pandas as pd

# loading the csv file
data = pd.read_csv('areas.csv')

# perform one sample t-test
t_statistic, p_value = stats.ttest_1samp(a=data, popmean=5000)
print(t_statistic , p_value)

Output:

[-0.79248301] [0.44346471]

Here

t_statistic is  -0.79248301

p-value is 0.44346471

As the p_value for the given problem is more than 0.05 which is the alpha value, we accept the null hypothesis and the alternative hypothesis is rejected.