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
# 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.
# 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)
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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.