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In this project, I conducted A/B tests to evaluate the mean district literacy rates of two states with Python.

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Conduct a A/B test with Python: Analyse the Literacy Rates of Two States for Funding Allocation

In this project, I conducted A/B tests to determine the mean district literacy rates for two states (STATE21 and STATE28). I used Python to simulate taking a random sample of 20 districts in each state and conducted a two-sample t-test based on the sample data.

The data used for the analysis (education_districtwise.csv) was accessed via https://www.kaggle.com/datasets/saswatsethda/districtwise-education-data/data

Summary of Results

There is a statistically significant difference between the mean district literacy rates of the two states. This analysis helps decide how to distribute government resources. Since STATE28 has a lower literacy rate, more resources should be allocated to improve the literacy rate of this state.

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In this project, I conducted A/B tests to evaluate the mean district literacy rates of two states with Python.

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