# ES.w1: Effect size calculation in the chi-squared test for goodness... In pwr: Basic Functions for Power Analysis

## Description

Compute effect size w for two sets of k probabilities P0 (null hypothesis) and P1 (alternative hypothesis)

## Usage

 `1` ```ES.w1(P0, P1) ```

## Arguments

 `P0` First set of k probabilities (null hypothesis) `P1` Second set of k probabilities (alternative hypothesis)

## Value

The corresponding effect size w

## Author(s)

Stephane CHAMPELY

## References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale,NJ: Lawrence Erlbaum.

pwr.chisq.test

## Examples

 ```1 2 3 4 5``` ```## Exercise 7.1 p. 249 from Cohen P0<-rep(1/4,4) P1<-c(0.375,rep((1-0.375)/3,3)) ES.w1(P0,P1) pwr.chisq.test(w=ES.w1(P0,P1),N=100,df=(4-1)) ```

### Example output

```[1] 0.2886751

Chi squared power calculation

w = 0.2886751
N = 100
df = 3
sig.level = 0.05
power = 0.6739834

NOTE: N is the number of observations
```

pwr documentation built on March 17, 2020, 5:11 p.m.