# How to Conduct a Two Sample T-Test in Python

In the realm of data science and statistics, hypothesis testing plays a pivotal role in making data-driven decisions. A common technique for hypothesis testing is the t-test, and in this article, we will focus on the Two Sample t-test and how to perform it using Python, complete with a practical example.

1. Understanding the Two Sample T-Test
2. Steps to Perform a Two Sample T-Test
3. Setting Up the Python Environment
4. Conducting a Two Sample T-Test in Python: An Illustrative Example
5. Conclusion

## 1. Understanding the Two Sample T-Test

The Two Sample t-test, often called the Independent t-test, is a statistical procedure used to determine whether the means of two independent groups are equal. This test is applicable when you have two independent data samples from two populations, and you want to compare their means.

In the context of hypothesis testing, the procedure starts with a null hypothesis (H0) and an alternative hypothesis (H1).

• The Null Hypothesis (H0): In the two-sample t-test, the null hypothesis assumes that the means of the two populations are equal.
• The Alternative Hypothesis (H1): Contrary to the null hypothesis, the alternative hypothesis assumes that the means of the two populations are not equal.

Through the Two Sample t-test, we ascertain whether to accept or reject the null hypothesis.

## 2. Steps to Perform a Two Sample T-Test

The steps to conduct a two-sample t-test are as follows:

1. Define the Hypotheses: State the null hypothesis and the alternative hypothesis based on your research question.
2. Choose a Significance Level: The significance level, typically denoted by alpha (α), is a threshold that determines when to reject the null hypothesis. The common choices for alpha are 0.05 (5%), 0.01 (1%), and 0.1 (10%).
3. Calculate the T-Statistic: The t-statistic is computed as the difference between the sample means divided by the standard error of the difference.
4. Compute the P-value: The p-value is the probability of getting a t-statistic as extreme as, or more than, the one you calculated, assuming the null hypothesis is true.
5. Make a Decision: Based on the p-value, if it’s less than the significance level (α), you reject the null hypothesis; otherwise, you fail to reject it.

## 3. Setting Up the Python Environment

Python is a preferred language for statistical analysis and data science, boasting numerous libraries for various statistical tests. To execute a two-sample t-test, you need to install the numpy package for numerical computation and scipy, which contains the statistical functions.

You can install these packages using pip:

pip install numpy scipy

After installing, import the necessary libraries:

import numpy as np
import scipy.stats as stats

## 4. Conducting a Two Sample T-Test in Python: An Illustrative Example

Consider a pharmaceutical company testing the effectiveness of a new drug. They select two independent groups – one group gets the drug, and the other gets a placebo. After a period, the company measures the health improvement score for the individuals in both groups.

Let’s generate some random health improvement scores for this example:

# Randomly generating health improvement scores
np.random.seed(0) # for reproducibility
drug_scores = np.random.normal(75, 5, 100)
placebo_scores = np.random.normal(70, 5, 100)

In this scenario, the null hypothesis is that the mean health improvement score is the same for both the drug and placebo groups, while the alternative hypothesis is that the means are different.

To perform the two-sample t-test, we’ll use the ttest_ind() function from the scipy.stats module:

# Perform two-sample t-test
t_statistic, p_value = stats.ttest_ind(drug_scores, placebo_scores)

print(f'T-statistic: {t_statistic}')
print(f'P-value: {p_value}')

After running the test, compare the p-value with the significance level. Let’s use α = 0.05:

alpha = 0.05
if p_value < alpha:
print("We reject the null hypothesis.")
else:
print("We fail to reject the null hypothesis.")

If the p-value is less than the significance level, we reject the null hypothesis and conclude that the drug has a significant effect on health improvement. If not, we fail to reject the null hypothesis, indicating that the drug does not have a significant effect compared to the placebo.

## 5. Conclusion

The Two Sample t-test is a powerful statistical tool for comparing the means of two independent groups. Python, with its extensive statistical libraries, makes it easy for data scientists and researchers to perform these tests.

While t-tests provide a formal way of comparing group means, it is crucial to remember that the results are not definitive proof. They should be combined with other research methods and domain knowledge for comprehensive analysis. It’s also important to verify that the data meets the requirements for a t-test (e.g., normal distribution, independence of observations) for the results to be reliable.