Performing a t-test in Python
To conduct a t-test in Python, all you have to do is specify the alternative hypothesis and indicate whether variances are roughly equal (homogeneous).
The ttest_ind()
function within scipy.stats
handles the rest. Below is the syntax:
Parameters:
a
— the first sample;b
— the second sample;equal_var
— set toTrue
if variances are approximately equal, andFalse
if they are not;alternative
— the type of alternative hypothesis:'two-sided'
— indicates that the means are not equal;'less'
— implies that the first mean is less than the second;'greater'
— implies that the first mean is greater than the second.
Return values:
statistic
— the value of the t statistic;pvalue
— the p-value.
We are interested in the pvalue
. If it is lower than α(usually 0.05), then the t statistic is in the critical region, so we should accept the alternative hypothesis. And if pvalue
is greater than α — we accept the null hypothesis that means are equal.
Here is an example of applying the t-test to our heights dataset:
Everything was clear?
Course Content
Learning Statistics with Python
2. Mean, Median and Mode with Python
4. Covariance vs Correlation
Learning Statistics with Python
Performing a t-test in Python
To conduct a t-test in Python, all you have to do is specify the alternative hypothesis and indicate whether variances are roughly equal (homogeneous).
The ttest_ind()
function within scipy.stats
handles the rest. Below is the syntax:
Parameters:
a
— the first sample;b
— the second sample;equal_var
— set toTrue
if variances are approximately equal, andFalse
if they are not;alternative
— the type of alternative hypothesis:'two-sided'
— indicates that the means are not equal;'less'
— implies that the first mean is less than the second;'greater'
— implies that the first mean is greater than the second.
Return values:
statistic
— the value of the t statistic;pvalue
— the p-value.
We are interested in the pvalue
. If it is lower than α(usually 0.05), then the t statistic is in the critical region, so we should accept the alternative hypothesis. And if pvalue
is greater than α — we accept the null hypothesis that means are equal.
Here is an example of applying the t-test to our heights dataset:
Everything was clear?