Perhaps one of the most widely used statistical hypothesis tests is the Student’s t test.
Because you may use this test yourself someday, it is important to have a deep understanding of how the test works. As a developer, this understanding is best achieved by implementing the hypothesis test yourself from scratch.
In this tutorial, you will discover how to implement the Student’s ttest statistical hypothesis test from scratch in Python.
After completing this tutorial, you will know:
 The Student’s ttest will comment on whether it is likely to observe two samples given that the samples were drawn from the same population.
 How to implement the Student’s ttest from scratch for two independent samples.
 How to implement the paired Student’s ttest from scratch for two dependent samples.
Let’s get started.
Tutorial Overview
This tutorial is divided into three parts; they are:
 Student’s tTest
 Student’s tTest for Independent Samples
 Student’s tTest for Dependent Samples
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Student’s tTest
The Student’s tTest is a statistical hypothesis test for testing whether two samples are expected to have been drawn from the same population.
It is named for the pseudonym “Student” used by William Gosset, who developed the test.
The test works by checking the means from two samples to see if they are significantly different from each other. It does this by calculating the standard error in the difference between means, which can be interpreted to see how likely the difference is, if the two samples have the same mean (the null hypothesis).
The t statistic calculated by the test can be interpreted by comparing it to critical values from the tdistribution. The critical value can be calculated using the degrees of freedom and a significance level with the percent point function (PPF).
We can interpret the statistic value in a twotailed test, meaning that if we reject the null hypothesis, it could be because the first mean is smaller or greater than the second mean. To do this, we can calculate the absolute value of the test statistic and compare it to the positive (right tailed) critical value, as follows:
 If abs(tstatistic) <= critical value: Accept null hypothesis that the means are equal.
 If abs(tstatistic) > critical value: Reject the null hypothesis that the means are equal.
We can also retrieve the cumulative probability of observing the absolute value of the tstatistic using the cumulative distribution function (CDF) of the tdistribution in order to calculate a pvalue. The pvalue can then be compared to a chosen significance level (alpha) such as 0.05 to determine if the null hypothesis can be rejected:
 If p > alpha: Accept null hypothesis that the means are equal.
 If p <= alpha: Reject null hypothesis that the means are equal.
In working with the means of the samples, the test assumes that both samples were drawn from a Gaussian distribution. The test also assumes that the samples have the same variance, and the same size, although there are corrections to the test if these assumptions do not hold. For example, see Welch’s ttest.
There are two main versions of Student’s ttest:
 Independent Samples. The case where the two samples are unrelated.
 Dependent Samples. The case where the samples are related, such as repeated measures on the same population. Also called a paired test.
Both the independent and the dependent Student’s ttests are available in Python via the ttest_ind() and ttest_rel() SciPy functions respectively.
Note: I recommend using these SciPy functions to calculate the Student’s ttest for your applications, if they are suitable. The library implementations will be faster and less prone to bugs. I would only recommend implementing the test yourself for learning purposes or in the case where you require a modified version of the test.
We will use the SciPy functions to confirm the results from our own version of the tests.
Note, for reference, all calculations presented in this tutorial are taken directly from Chapter 9 “t Tests” in “Statistics in Plain English“, Third Edition, 2010. I mention this because you may see the equations with different forms, depending on the reference text that you use.
Student’s tTest for Independent Samples
We’ll start with the most common form of the Student’s ttest: the case where we are comparing the means of two independent samples.
Calculation
The calculation of the tstatistic for two independent samples is as follows:

t = observed difference between sample means / standard error of the difference between the means 
or

t = (mean(X1) – mean(X2)) / sed 
Where X1 and X2 are the first and second data samples and sed is the standard error of the difference between the means.
The standard error of the difference between the means can be calculated as follows:

sed = sqrt(se1^2 + se2^2) 
Where se1 and se2 are the standard errors for the first and second datasets.
The standard error of a sample can be calculated as:
Where se is the standard error of the sample, std is the sample standard deviation, and n is the number of observations in the sample.
These calculations make the following assumptions:
 The samples are drawn from a Gaussian distribution.
 The size of each sample is approximately equal.
 The samples have the same variance.
Implementation
We can implement these equations easily using functions from the Python standard library, NumPy and SciPy.
Let’s assume that our two data samples are stored in the variables data1 and data2.
We can start off by calculating the mean for these samples as follows:

# calculate means mean1, mean2 = mean(data1), mean(data2) 
We’re halfway there.
Now we need to calculate the standard error.
We can do this manually, first by calculating the sample standard deviations:

# calculate sample standard deviations std1, std2 = std(data1, ddof=1), std(data2, ddof=1) 
And then the standard errors:

# calculate standard errors n1, n2 = len(data1), len(data2) se1, se2 = std1/sqrt(n1), std2/sqrt(n2) 
Alternately, we can use the sem() SciPy function to calculate the standard error directly.

# calculate standard errors se1, se2 = sem(data1), sem(data2) 
We can use the standard errors of the samples to calculate the “standard error on the difference between the samples“:

# standard error on the difference between the samples sed = sqrt(se1**2.0 + se2**2.0) 
We can now calculate the t statistic:

# calculate the t statistic t_stat = (mean1 – mean2) / sed 
We can also calculate some other values to help interpret and present the statistic.
The number of degrees of freedom for the test is calculated as the sum of the observations in both samples, minus two.

# degrees of freedom df = n1 + n2 – 2 
The critical value can be calculated using the percent point function (PPF) for a given significance level, such as 0.05 (95% confidence).
This function is available for the t distribution in SciPy, as follows:

# calculate the critical value alpha = 0.05 cv = t.ppf(1.0 – alpha, df) 
The pvalue can be calculated using the cumulative distribution function on the tdistribution, again in SciPy.

# calculate the pvalue p = (1 – t.cdf(abs(t_stat), df)) * 2 
Here, we assume a twotailed distribution, where the rejection of the null hypothesis could be interpreted as the first mean is either smaller or larger than the second mean.
We can tie all of these pieces together into a simple function for calculating the ttest for two independent samples:

# function for calculating the ttest for two independent samples def independent_ttest(data1, data2, alpha): # calculate means mean1, mean2 = mean(data1), mean(data2) # calculate standard errors se1, se2 = sem(data1), sem(data2) # standard error on the difference between the samples sed = sqrt(se1**2.0 + se2**2.0) # calculate the t statistic t_stat = (mean1 – mean2) / sed # degrees of freedom df = len(data1) + len(data2) – 2 # calculate the critical value cv = t.ppf(1.0 – alpha, df) # calculate the pvalue p = (1.0 – t.cdf(abs(t_stat), df)) * 2.0 # return everything return t_stat, df, cv, p 
Worked Example
In this section we will calculate the ttest on some synthetic data samples.
First, let’s generate two samples of 100 Gaussian random numbers with the same variance of 5 and differing means of 50 and 51 respectively. We will expect the test to reject the null hypothesis and find a significant difference between the samples:

# seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 
We can calculate the ttest on these samples using the built in SciPy function ttest_ind(). This will give us a tstatistic value and a pvalue to compare to, to ensure that we have implemented the test correctly.
The complete example is listed below.

# Student’s ttest for independent samples from numpy.random import seed from numpy.random import randn from scipy.stats import ttest_ind # seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 # compare samples stat, p = ttest_ind(data1, data2) print(‘t=%.3f, p=%.3f’ % (stat, p)) 
Running the example, we can see a tstatistic value and p value.
We will use these as our expected values for the test on these data.
We can now apply our own implementation on the same data, using the function defined in the previous section.
The function will return a tstatistic value and a critical value. We can use the critical value to interpret the t statistic to see if the finding of the test is significant and that indeed the means are different as we expected.

# interpret via critical value if abs(t_stat) <= cv: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) 
The function also returns a pvalue. We can interpret the pvalue using an alpha, such as 0.05 to determine if the finding of the test is significant and that indeed the means are different as we expected.

# interpret via pvalue if p > alpha: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) 
We expect that both interpretations will always match.
The complete example is listed below.
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# ttest for independent samples from math import sqrt from numpy.random import seed from numpy.random import randn from numpy import mean from scipy.stats import sem from scipy.stats import t
# function for calculating the ttest for two independent samples def independent_ttest(data1, data2, alpha): # calculate means mean1, mean2 = mean(data1), mean(data2) # calculate standard errors se1, se2 = sem(data1), sem(data2) # standard error on the difference between the samples sed = sqrt(se1**2.0 + se2**2.0) # calculate the t statistic t_stat = (mean1 – mean2) / sed # degrees of freedom df = len(data1) + len(data2) – 2 # calculate the critical value cv = t.ppf(1.0 – alpha, df) # calculate the pvalue p = (1.0 – t.cdf(abs(t_stat), df)) * 2.0 # return everything return t_stat, df, cv, p
# seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 # calculate the t test alpha = 0.05 t_stat, df, cv, p = independent_ttest(data1, data2, alpha) print(‘t=%.3f, df=%d, cv=%.3f, p=%.3f’ % (t_stat, df, cv, p)) # interpret via critical value if abs(t_stat) <= cv: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) # interpret via pvalue if p > alpha: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) 
Running the example first calculates the test.
The results of the test are printed, including the tstatistic, the degrees of freedom, the critical value, and the pvalue.
We can see that both the tstatistic and pvalue match the outputs of the SciPy function. The test appears to be implemented correctly.
The tstatistic and the pvalue are then used to interpret the results of the test. We find that as we expect, there is sufficient evidence to reject the null hypothesis, finding that the sample means are likely different.

t=2.262, df=198, cv=1.653, p=0.025 Reject the null hypothesis that the means are equal. Reject the null hypothesis that the means are equal. 
Student’s tTest for Dependent Samples
We can now look at the case of calculating the Student’s ttest for dependent samples.
This is the case where we collect some observations on a sample from the population, then apply some treatment, and then collect observations from the same sample.
The result is two samples of the same size where the observations in each sample are related or paired.
The ttest for dependent samples is referred to as the paired Student’s ttest.
Calculation
The calculation of the paired Student’s ttest is similar to the case with independent samples.
The main difference is in the calculation of the denominator.

t = (mean(X1) – mean(X2)) / sed 
Where X1 and X2 are the first and second data samples and sed is the standard error of the difference between the means.
Here, sed is calculated as:
Where sd is the standard deviation of the difference between the dependent sample means and n is the total number of paired observations (e.g. the size of each sample).
The calculation of sd first requires the calculation of the sum of the squared differences between the samples:

d1 = sum (X1[i] – X2[i])^2 for i in n 
It also requires the sum of the (non squared) differences between the samples:

d2 = sum (X1[i] – X2[i]) for i in n 
We can then calculate sd as:

sd = sqrt((d1 – (d2**2 / n)) / (n – 1)) 
That’s it.
Implementation
We can implement the calculation of the paired Student’s ttest directly in Python.
The first step is to calculate the means of each sample.

# calculate means mean1, mean2 = mean(data1), mean(data2) 
Next, we will require the number of pairs (n). We will use this in a few different calculations.

# number of paired samples n = len(data1) 
Next, we must calculate the sum of the squared differences between the samples, as well as the sum differences.

# sum squared difference between observations d1 = sum([(data1[i]–data2[i])**2 for i in range(n)]) # sum difference between observations d2 = sum([data1[i]–data2[i] for i in range(n)]) 
We can now calculate the standard deviation of the difference between means.

# standard deviation of the difference between means sd = sqrt((d1 – (d2**2 / n)) / (n – 1)) 
This is then used to calculate the standard error of the difference between the means.

# standard error of the difference between the means sed = sd / sqrt(n) 
Finally, we have everything we need to calculate the t statistic.

# calculate the t statistic t_stat = (mean1 – mean2) / sed 
The only other key difference between this implementation and the implementation for independent samples is the calculation of the number of degrees of freedom.

# degrees of freedom df = n – 1 
As before, we can tie all of this together into a reusable function. The function will take two paired samples and a significance level (alpha) and calculate the tstatistic, number of degrees of freedom, critical value, and pvalue.
The complete function is listed below.
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# function for calculating the ttest for two dependent samples def dependent_ttest(data1, data2, alpha): # calculate means mean1, mean2 = mean(data1), mean(data2) # number of paired samples n = len(data1) # sum squared difference between observations d1 = sum([(data1[i]–data2[i])**2 for i in range(n)]) # sum difference between observations d2 = sum([data1[i]–data2[i] for i in range(n)]) # standard deviation of the difference between means sd = sqrt((d1 – (d2**2 / n)) / (n – 1)) # standard error of the difference between the means sed = sd / sqrt(n) # calculate the t statistic t_stat = (mean1 – mean2) / sed # degrees of freedom df = n – 1 # calculate the critical value cv = t.ppf(1.0 – alpha, df) # calculate the pvalue p = (1.0 – t.cdf(abs(t_stat), df)) * 2.0 # return everything return t_stat, df, cv, p 
Worked Example
In this section, we will use the same dataset in the worked example as we did for the independent Student’s ttest.
The data samples are not paired, but we will pretend they are. We expect the test to reject the null hypothesis and find a significant difference between the samples.

# seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 
As before, we can evaluate the test problem with the SciPy function for calculating a paired ttest. In this case, the ttest_rel() function.
The complete example is listed below.

# Paired Student’s ttest from numpy.random import seed from numpy.random import randn from scipy.stats import ttest_rel # seed the random number generator seed(1) # generate two independent samples data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 # compare samples stat, p = ttest_rel(data1, data2) print(‘Statistics=%.3f, p=%.3f’ % (stat, p)) 
Running the example calculates and prints the tstatistic and the pvalue.
We will use these values to validate the calculation of our own paired ttest function.

Statistics=2.372, p=0.020 
We can now test our own implementation of the paired Student’s ttest.
The complete example, including the developed function and interpretation of the results of the function, is listed below.
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# ttest for dependent samples from math import sqrt from numpy.random import seed from numpy.random import randn from numpy import mean from scipy.stats import t
# function for calculating the ttest for two dependent samples def dependent_ttest(data1, data2, alpha): # calculate means mean1, mean2 = mean(data1), mean(data2) # number of paired samples n = len(data1) # sum squared difference between observations d1 = sum([(data1[i]–data2[i])**2 for i in range(n)]) # sum difference between observations d2 = sum([data1[i]–data2[i] for i in range(n)]) # standard deviation of the difference between means sd = sqrt((d1 – (d2**2 / n)) / (n – 1)) # standard error of the difference between the means sed = sd / sqrt(n) # calculate the t statistic t_stat = (mean1 – mean2) / sed # degrees of freedom df = n – 1 # calculate the critical value cv = t.ppf(1.0 – alpha, df) # calculate the pvalue p = (1.0 – t.cdf(abs(t_stat), df)) * 2.0 # return everything return t_stat, df, cv, p
# seed the random number generator seed(1) # generate two independent samples (pretend they are dependent) data1 = 5 * randn(100) + 50 data2 = 5 * randn(100) + 51 # calculate the t test alpha = 0.05 t_stat, df, cv, p = dependent_ttest(data1, data2, alpha) print(‘t=%.3f, df=%d, cv=%.3f, p=%.3f’ % (t_stat, df, cv, p)) # interpret via critical value if abs(t_stat) <= cv: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) # interpret via pvalue if p > alpha: print(‘Accept null hypothesis that the means are equal.’) else: print(‘Reject the null hypothesis that the means are equal.’) 
Running the example calculates the paired ttest on the sample problem.
The calculated tstatistic and pvalue match what we expect from the SciPy library implementation. This suggests that the implementation is correct.
The interpretation of the ttest statistic with the critical value, and the pvalue with the significance level both find a significant result, rejecting the null hypothesis that the means are equal.

t=2.372, df=99, cv=1.660, p=0.020 Reject the null hypothesis that the means are equal. Reject the null hypothesis that the means are equal. 
Extensions
This section lists some ideas for extending the tutorial that you may wish to explore.
 Apply each test to your own contrived sample problem.
 Update the independent test and add the correction for samples with different variances and sample sizes.
 Perform a code review of one of the tests implemented in the SciPy library and summarize the differences in the implementation details.
If you explore any of these extensions, I’d love to know.
Further Reading
This section provides more resources on the topic if you are looking to go deeper.
Books
API
Articles
Summary
In this tutorial, you discovered how to implement the Student’s ttest statistical hypothesis test from scratch in Python.
Specifically, you learned:
 The Student’s ttest will comment on whether it is likely to observe two samples given that the samples were drawn from the same population.
 How to implement the Student’s ttest from scratch for two independent samples.
 How to implement the paired Student’s ttest from scratch for two dependent samples.
Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.
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