power-analysis 0.1.0

This package allows you to perform power analysis computations

power-analysis 💪🔍

power-analysis is a Python package for performing power analysis and calculating sample sizes for statistical models. The package provides classes for defining statistical models, performing power analysis, and calculating sample sizes for two-sample t-tests.

Installation 📥

You can install the power-analysis package using pip:

pip install power-analysis

Usage 🧑‍💻

To use the power-analysis package, you first need to define a statistical model using a Python function that takes a sample size as input and returns the estimated effect size of the model. For example:

def my_model(n):
    # define your model here
    effect_size = ...
    return effect_size

You can then create a Model object using this function:

from power_analysis import Model

model = Model(my_model)

You can perform a power analysis for this model using the PowerAnalysis class:

from power_analysis import PowerAnalysis

power_analysis = PowerAnalysis(model, n=100, alpha=0.05, power=0.8, effect_size=0.5, iterations=1000)
results = power_analysis.results()

This will calculate the minimum detectable effect and sample size required to achieve a power of 0.8 for the given model, starting with an initial sample size of 100.

You can also calculate the sample size required for a two-sample t-test using the TTestSampleSize class:

from power_analysis import TTestSampleSize

mean_diff = 1.5
sd = 2.0
alpha = 0.05
power = 0.8

sample_size = TTestSampleSize(mean_diff, sd, alpha, power, n=None)
n = sample_size.result()

This will calculate the sample size required to achieve a power of 0.8 for a two-sample t-test with a difference in means of 1.5, a standard deviation of 2.0, and a significance level of 0.05.

Contributing 🤝

Contributions to power-analysis are welcome! If you find a bug or would like to suggest a new feature, please open an issue on GitHub.

License 📜

power-analysis is licensed under the MIT license. See LICENSE for more information.