Metadata-Version: 2.4
Name: np-stats
Version: 0.1.0
Summary: Educational non-parametric statistics lab toolkit with printed interpretations
Author: Manohar
License: MIT
Project-URL: Homepage, https://pypi.org/project/np-stats/
Keywords: statistics,nonparametric,hypothesis-testing,education,lab
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.21.0
Requires-Dist: scipy>=1.10.0

# np-stats

`np-stats` is an educational non-parametric statistics lab toolkit.
It wraps common SciPy tests and prints both numeric results and plain-English
interpretations.

Install:

```bash
pip install np-stats
```

Import:

```python
import np_stats as nps
```

Example:

```python
result = nps.mann_whitney_test(
    [23, 45, 12, 67, 34, 56, 22, 48],
    [34, 56, 78, 45, 89, 62, 41, 57],
)
```

Every test returns a dictionary and prints the result by default. Use
`print_result=False` to suppress printing.

If you want the function to ask for required parameters interactively, call it
with `ask=True` and omit the data arguments:

```python
nps.binomial_test(ask=True)
```

Included tests:

- Binomial test
- One-sample Kolmogorov-Smirnov test
- Chi-square goodness-of-fit
- McNemar test
- Wilcoxon signed-rank test
- Sign test
- Paired permutation test
- Fisher exact test
- Chi-square test for independent samples
- Median test for two or k samples
- Mann-Whitney U test
- Two-sample Kolmogorov-Smirnov test
- Siegel-Tukey test
- Two-independent-samples permutation test
- Cochran Q test
- Friedman test
- Page trend test
- Chi-square test for k independent samples
- Kruskal-Wallis test
- Jonckheere-Terpstra trend test
