Hypothesis Testing is a statistical method used to make inferences or decisions about a population based on sample data. It starts with a null hypothesis (H0), which represents a default stance or no effect, and an alternative hypothesis (H1 or Ha), which represents what we aim to prove or expect to find. The process involves using sample data to determine whether to reject the null hypothesis in favor of the alternative hypothesis, based on the likelihood of observing the sample data under the null hypothesis.

Hypothesis Testing is a fundamental process in data science for making data-driven decisions and inferences about populations based on sample data. Below is the process we can follow for the task of Hypothesis Testing:

1. Gather the necessary data required for the hypothesis test.
2. Define Null (H0) and Alternative Hypothesis (H1 or Ha).
3. Choose the Significance Level (α): This is the probability of rejecting the null hypothesis when it is true.
4. Select the appropriate statistical tests: Examples include t-tests for comparing means, chi-square tests for categorical data, and ANOVA for comparing means across more than two groups.
5. Perform the chosen statistical test on your data.
6. Determine the p-value and interpret the results of your statistical tests.

To get started with Hypothesis Testing, we need appropriate data. You can download the dataset from this link.

Ensure you have the following Python libraries installed:

• pandas
• scipy

You can install them using pip:

pip install pandas scipy

Here's a basic example to demonstrate how to perform hypothesis testing using Python:

1. Importing the necessary libraries

   import pandas as pd
   from scipy.stats import ttest_ind

2. Loading the dataset

   df = pd.read_csv("path_to_your_dataset.csv")
   print(df.head())

3. Defining the Hypotheses

   • Null Hypothesis (H0): There is no significant difference between the means of two groups.
   • Alternative Hypothesis (H1): There is a significant difference between the means of two groups.

4. Choosing the Significance Level

   alpha = 0.05

5. Selecting and Performing the Statistical Test

   Assuming we are comparing the means of two groups using a t-test:

   group1 = df[df['Group'] == 'Group1']['Value']
   group2 = df[df['Group'] == 'Group2']['Value']
   
   t_stat, p_value = ttest_ind(group1, group2)
   print(f"T-Statistic: {t_stat}, P-Value: {p_value}")

6. Interpreting the Results

   if p_value < alpha:
       print("Reject the null hypothesis (H0)")
   else:
       print("Fail to reject the null hypothesis (H0)")

Hypothesis Testing is a powerful tool for data scientists and statisticians. By following the process outlined above, you can make informed decisions based on your data. This repository aims to provide a clear and concise guide to performing hypothesis testing using Python.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.

Feel free to explore, experiment, and contribute to this repository. Happy testing!