Overview

Assumption Sheriff Package 👮‍♂️⭐

AssumptionSheriff is a comprehensive Python package designed to validate statistical test assumptions. It provides automated checking of common statistical assumptions and offers recommendations when assumptions are violated. The packges supports 16 commonly used statistical tests, more tests will be added in the coming package updates.

Unfortunately, it is quite common that published health research do not fully report or validate the underlying assumptions of statistical tests utilized as reported by many articles, see:

• Hoekstra et al. (2012) "Are Assumptions of Well-Known Statistical Techniques Checked, and Why (Not)?" https://doi.org/10.3389/fpsyg.2012.00137

• Patino et. al (2018) "Meeting the assumptions of statistical tests: an important and often forgotten step to reporting valid results" https://doi.org/10.1590/S1806-37562018000000303

• Nielsen et. al (2019) "Assessing assumptions for statistical analyses in randomised clinical trials" https://doi.org/10.1136/bmjebm-2019-111174

To bridge this gap, Assumption Sheriff automates the process of checking statistical test assumptions, providing clear feedback and suggestions for alternative approaches when violations occur.

Supported Statistical Tests

AssumptionSheriff supports assumption checking for the following statistical tests:

1. Independent Samples t-test t_test_ind
2. Repeated-Measures ANOVA repeated_anova
3. Logistic Regression logistic
4. Factorial ANOVA (Two-way ANOVA) factorial_anova
5. One-Way ANOVA (one_way_anova) one_way_anova
6. Pearson Correlation pearson_correlation
7. Paired t-test paired_ttest
8. Chi-Square Test of Independence chi_square_independence
9. Multiple Regression (multiple_regression)
10. Two-Way ANOVA two_way_anova
11. Kaplan-Meier Analysis kaplan_meier
12. Cox Proportional Hazards cox_ph
13. Poisson Regression poisson
14. Spearman Correlation spearman
15. Wilcoxon Signed-Rank Test wilcoxon_signed_rank
16. MANOVA (Multivariate Analysis of Variance) manova

More tests to be added in future vesrions.

Key Features

1. Comprehensive assumption checking
2. Recommendations for alternative methods
3. Flexible integration
4. Commonly used test support

Citation

If you use AssumptionSheriff in your research, please cite:

@software{assumptionsheriff2024,
    title = {AssumptionSheriff: A Python Package for Statistical Assumption Checking},
    author = {Mohsen Askar},
    e-mail = {ceaser198511@gmail.com},
    year = {2024},
    url = {https://pypi.org/project/assumption-sheriff/}
}