Calculate cohen’s d for 1 sample. See Using_cohens_d.ipynb for a notebook of given examples.
Syntax
d = cD.cohensd_1samp(data)
d = cD.cohensd_1samp(data, Name=Value)
Description
A
d = cD.cohensd_1samp(data) returns cohen’s d for 1 sample comparing to mu=0. example
B
d = cD.cohensd_1samp(data, Name=Value) returns cohen’s d for 1 sample with additional options specified by one name-value pair arguments. For example, you can compare to a mean of 15 (mu=15). example
Examples
Example 1
Generate some random data and find cohen’s d.
import numpy as np
data = np.random.normal(0, 1, size=(100,))
d = cD.cohensd_1samp(data)
d = 0.058009400346186867
Example 2
Generate some random data and find cohen’s d when comparing to mean of 15.
import numpy as np
data = np.random.normal(0, 1, size=(100,))
d = cD.cohensd_1samp(data, mu=15)
d = 15.881558105056795
data
Data vector.
Vector of data to calculate 1 sample cohen’s d.
Data Types: (numeric)
Name-Value Arguments
Specified optional pairs of Name=Value arguments. Name is the is the argument name and Value is the corresponding value. You can specify several name and value pair arguments in any order as Name1=Value1,...,NameN=ValueN.
Example: mu=15 specifies comparison to a mean of 15.
mu
Mean to compare against (default=0)
Value to subtract from mean of data.
Data Types: (scalar, float, numeric)
Output
d
Effect size.
Cohen’s d effect size for 1 sample test.
Data Types: (scalar, float, numeric)
More About
Lecture
I refrained from outputting size of effect (e.g., ‘small’, ‘medium’, ‘large’) because these are arbitrary and should be thought of as such.
Tips
Issues and Discussion
Issues and Discussion.
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