grim 0.0.4

An implementation of the GRIM test, in Python

The GRIM test

An implementation of the GRIM test, in python

Beta: Work in progress

Introduction

This package is based on the GRIM (Granularity-Related Inconsistency of Means) test first highlighted by Heathers & Brown in their 2016 paper.

The test makes use of a simple numerical property to identify if the mean of integer values has been correctly calculated.

You don't need the original integer values. You just need the mean and the number (n) of items in the list.

What about rounding?

Often the 'mean' you are testing has previously been rounded. You can check if the mean is consistent with a particular rounding type by including that as an argument.

This implementation supports all the rounding types currently found in Python 3.8's decimal implementation. (They are: ROUND_CEILING, ROUND_DOWN, ROUND_FLOOR¶, ROUND_HALF_DOWN, ROUND_HALF_EVEN, ROUND_HALF_UP, ROUND_UP, ROUND_05UP)

If no rounding type is included then the test assumes the it should use ROUND_HALF_UP.

Example:

from grim import mean_tester
import decimal

# mean is 11.09 and n is 21
print(mean_tester.consistency_check('11.09', '21', decimal.ROUND_HALF_UP))

This will return False as the mean could not be correct given a list of 21 integers (and using ROUND_HALF_UP rounding.)

You can pass in the numbers as Strings or Decimals, this avoids floating point accuracy issues that are more likely to occur when using a 'float'.

How can I find out more?

James Heathers has published articles that explain how the technique works and how he used it to expose inconsistencies in scientific papers.