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Tools for statistical analysis of A/B test results

Project description

Tools for statistical analysis of A/B test results.

ABBA provides several statistical tools for analysis of binomial data, typically resulting from A/B tests:

  • Wald and Agresti-Coull confidence intervals on binomial proportions
  • Confidence intervals on the difference and ratio of two binomial proportions
  • Hypothesis tests for inequality of two binomial proportions
  • Multiple test correction for control of familywise error rate

Some simple example usage:

>>> import abba.stats
>>> abba.stats.confidence_interval_on_proportion(
...     num_successes=50, num_trials=200, confidence_level=0.99)
ValueWithInterval(value=0.25, lower_bound=0.17962262748069852, upper_bound=0.33643200973247306)

>>> experiment = abba.stats.Experiment(
...     num_trials=5, baseline_num_successes=50, baseline_num_trials=200)
>>> results = experiment.get_results(num_successes=70, num_trials=190)
>>> results.relative_improvement
ValueWithInterval(value=0.4736842105263157, lower_bound=-0.014130868125315277, upper_bound=0.90421878236700903)
>>> results.two_tailed_p_value

ABBA requires SciPy for underlying statistical functions.

For more info, see the docstrings, unit tests, and the ABBA website (including an interactive Javascript version) at

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