tea-tasting 0.2.0

A Python package for the statistical analysis of A/B tests.

tea-tasting: statistical analysis of A/B tests

tea-tasting is a Python package for the statistical analysis of A/B tests featuring:

• Student's t-test, Z-test, Bootstrap, and quantile metrics out of the box.
• Extensible API: define and use statistical tests of your choice.
• Delta method for ratio metrics.
• Variance reduction with CUPED/CUPAC (also in combination with the delta method for ratio metrics).
• Confidence intervals for both absolute and percentage change.
• Sample ratio mismatch check.
• Power analysis.
• Multiple hypothesis testing (family-wise error rate and false discovery rate).

tea-tasting calculates statistics directly within data backends such as BigQuery, ClickHouse, PostgreSQL, Snowflake, Spark, and 20+ other backends supported by Ibis. This approach eliminates the need to import granular data into a Python environment, though Pandas DataFrames are also supported.

Check out the blog post explaining the advantages of using tea-tasting for the analysis of A/B tests.

Installation

pip install tea-tasting

Basic example

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)
#>             metric control treatment rel_effect_size rel_effect_size_ci pvalue
#>  sessions_per_user    2.00      1.98          -0.66%      [-3.7%, 2.5%]  0.674
#> orders_per_session   0.266     0.289            8.8%      [-0.89%, 19%] 0.0762
#>    orders_per_user   0.530     0.573            8.0%       [-2.0%, 19%]  0.118
#>   revenue_per_user    5.24      5.73            9.3%       [-2.4%, 22%]  0.123

Learn more in the detailed user guide. Additionally, see the guides on data backends, power analysis, multiple hypothesis testing, and custom metrics.

Roadmap

• Support more dataframes with Narwhals.
• A/A tests and simulations.
• More statistical tests:
  • Asymptotic and exact tests for frequency data.
  • Mann–Whitney U test.
• Sequential testing.

Package name

The package name "tea-tasting" is a play on words that refers to two subjects:

• Lady tasting tea is a famous experiment which was devised by Ronald Fisher. In this experiment, Fisher developed the null hypothesis significance testing framework to analyze a lady's claim that she could discern whether the tea or the milk was added first to the cup.
• "tea-tasting" phonetically resembles "t-testing" or Student's t-test, a statistical test developed by William Gosset.