# measure-noise
Measure how our data deviates from normal distribution
|Branch |Status | Coverage |
|------------|---------|----------|
|master | [](https://travis-ci.org/mozilla/measure-noise) | |
|dev | [](https://travis-ci.org/mozilla/measure-noise) | [](https://coveralls.io/github/mozilla/measure-noise) |
## Install
pip install measure-noise
## Usage
The `deviance()` method will return a `(description, score)` pair describing how the samples deviate from a normal distribution, and by how much. This is intended to screen samples for use in the t-test, and other statistics, that assume a normal distribution.
* `SKEWED` - samples are heavily to one side of the mean
* `OUTLIERS` - there are more outliers than would be expected from normal distribution
* `MODAL` - few samples are near the mean (probably bimodal)
* `OK` - no egregious deviation from normal
* `N/A` - not enough data to make a conclusion (aka `OK`)
#### Example
from measure_noise import deviance
>>> desc, score = deviance([1,2,3,4,5,6,7,8])
>>> desc
'OK'
## Development
git clone https://github.com/mozilla/measure-noise.git
cd measure-noise
pip install -r requirements.txt
pip install or tests/requirements.txt
python -m unittest discover tests
## Windows
You must download the `scipy` and `numpy` binary packages.
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"description": "# measure-noise\nMeasure how our data deviates from normal distribution\n\n\n|Branch |Status | Coverage |\n|------------|---------|----------|\n|master | [](https://travis-ci.org/mozilla/measure-noise) | |\n|dev | [](https://travis-ci.org/mozilla/measure-noise) | [](https://coveralls.io/github/mozilla/measure-noise) |\n\n\n## Install\n\n pip install measure-noise\n\n## Usage\n\nThe `deviance()` method will return a `(description, score)` pair describing how the samples deviate from a normal distribution, and by how much. This is intended to screen samples for use in the t-test, and other statistics, that assume a normal distribution.\n\n* `SKEWED` - samples are heavily to one side of the mean\n* `OUTLIERS` - there are more outliers than would be expected from normal distribution\n* `MODAL` - few samples are near the mean (probably bimodal)\n* `OK` - no egregious deviation from normal\n* `N/A` - not enough data to make a conclusion (aka `OK`)\n\n#### Example\n\n from measure_noise import deviance\n\n\t>>> desc, score = deviance([1,2,3,4,5,6,7,8])\n >>> desc\n 'OK'\n\n## Development\n\n git clone https://github.com/mozilla/measure-noise.git\n cd measure-noise\n pip install -r requirements.txt\n pip install or tests/requirements.txt\n python -m unittest discover tests \n\n## Windows\n\nYou must download the `scipy` and `numpy` binary packages. \n",
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