When it comes to decision-making based on data, AB testing is an essential method in the toolkit of data scientists and analysts. Today, we’ll explore how to utilize the **tea-tasting** Python package for a comprehensive statistical analysis of AB tests, while making it accessible even if you are not a statistician!

What is **tea-tasting**?

**tea-tasting** is a Python package specifically designed for the statistical analysis of AB tests. Think of it as a refined tea blend that combines multiple statistical methods such as Students t-test, Z-test, and Bootstrap to beautifully brew insights from your experimental data.

Getting Started: Installation

Let’s jump in! The first step is installing the package. You can do this easily with pip:

bash
pip install tea-tasting

Basic Example

Now, let’s see how to use **tea-tasting** with a simple example. Imagine you’re a tea connoisseur conducting an experiment to understand user interactions with your tea shop app. Here’s how you can run an experiment analysis:

python
import tea_tasting as tt

data = tt.make_users_data(seed=42)

experiment = tt.Experiment(
    sessions_per_user=tt.Mean(sessions),
    orders_per_session=tt.RatioOfMeans(orders, sessions),
    orders_per_user=tt.Mean(orders),
    revenue_per_user=tt.Mean(revenue),
)

result = experiment.analyze(data)
print(result)

This code snippet sets up a scenario where you’re examining user sessions, orders, and revenue in relation to two different versions of your tea-ordering application. The result gives you metrics like relative effect size and p-value to guide your decisions.

Understanding the Output

To further illustrate the analysis, here’s a breakdown of the results using an analogy. Imagine you’re tasting two different kinds of tea:

• Sessions per User: You observed that users engaged similarly with both versions, reflected in a very small relative effect size (-0.66%) with a high p-value (0.674), suggesting no reliable difference in user engagement.
• Orders per Session: There’s an 8.8% increase in orders for the treatment group, but since the p-value is around 0.0762, it teeters on the edge of statistical significance—like savoring the last sip of a delightful tea that might hint at a better blend.
• Revenue per User: An observed 9.3% increase with a p-value of 0.123 indicates potential, but again, emphasize caution, just as you wouldn’t declare a tea superior based only on a few tastes!

Troubleshooting Ideas

If you encounter any issues while using **tea-tasting**, here are a few troubleshooting steps:

• Ensure that all your dependencies are installed and updated. You can check this by running pip list.
• If the analysis throws errors related to data shapes, verify that your dataset aligns with the expected input formats defined in the user guide.
• Cross-reference the provided examples in the user guide to spot discrepancies in your code.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

What’s Next? Roadmap

The **tea-tasting** package is still brewing exciting advancements:

• Support for multiple hypothesis testing methods to control family-wise error rates.
• AA tests and simulations for better validation.
• Inclusion of more statistical tests such as the Mann-Whitney U test.

The Name Behind the Package

Perhaps you’re curious about the clever choice of name? **tea-tasting** reflects two significant concepts:

• The historical “Lady Tasting Tea” experiment, which laid the groundwork for modern hypothesis testing.
• The phonetic resemblance to t-testing, a core statistical measure for comparing means.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Wrap-Up

Congratulations! You’ve learned how to efficiently analyze AB tests using **tea-tasting**. As you refine your skills, remember the importance of accuracy and careful analysis, just like a true tea connoisseur assessing the perfect brew.