Basic Tutorial

The following tutorial demonstrates how to perform a robust hypothesis test using 20% trimmed means and the bootstrap-t test. The data correspond to a 2 (between-subjects) x 3 (within-subjects) factorial design.

Getting your data into Hypothesize

In Hypothesize, input data are always specified as a Pandas DataFrame or Series. In this example, we have a 2x3 factorial design so the data would take the form of a six-column DataFrame (i.e., J levels x K levels). Using Pandas you can read your data into Python and use one of the appropriate functions from Hypothesize. In this case we will use the function bwmcp but there are many others to choose from.

"What about my column names?"

Don't worry, Hypothesize doesn't make use of your column names. Feel free to name them however you like!

import pandas as pd

df=pd.read_csv('my_data.csv')

df.head() 

cell_1_1	cell_1_2	cell_1_3	cell_2_1	cell_2_2	cell_2_3
0.04	0.90	0.79	0.51	0.33	0.23
0.76	0.29	0.84	0.03	0.5	0.73
0.71	0.59	0.11	0.89	0.76	0.04
0.17	0.26	0.88	0.28	0.1	0.21
0.95	0.22	0.83	0.59	0.65	0.20

from hypothesize.compare_groups_with_two_factors import bwmcp

results=bwmcp(J=2, K=3, x=df)

Examining your results

The results are returned as a Python Dictionary containing simple Python objects or DataFrames (when the results are best given as a matrix). For example, here are the previously computed results for the interaction returned as a DataFrame.

results['factor_AB']

con_num	psihat	se	test	crit_value	p_value
0	-0.100698	0.126135	-0.798336	2.3771	0.410684
1	-0.037972	0.151841	-0.250078	2.3771	0.804674
2	0.0627261	0.135392	0.463291	2.3771	0.659432