From 06fdf34120b4b71917b9bc3c220ac01b49b9f91f Mon Sep 17 00:00:00 2001
From: Philip Waggoner <31326382+pdwaggoner@users.noreply.github.com>
Date: Tue, 19 Sep 2023 10:36:06 -0600
Subject: [PATCH] Update statistical_power.Rmd

---
 vignettes/statistical_power.Rmd | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/vignettes/statistical_power.Rmd b/vignettes/statistical_power.Rmd
index 6a663d1b..1aba1288 100644
--- a/vignettes/statistical_power.Rmd
+++ b/vignettes/statistical_power.Rmd
@@ -112,10 +112,7 @@ Given the simplicity of this example and the prevalence of Cohen's $d$, we will
 The first approach is the simplest. As previously hinted at, there is a vast literature on different effect size calculations for different applications. So, if you don't want to track down a specific one, or are unaware of options, you can simply pass the statistical test object to `effectsize()`, and either select the `type`, or leave it blank for "cohens_d", which is the default option. 
 
 ```{r warning = FALSE}
-effectsize(
-  t, 
-  type = "cohens_d"
-)
+effectsize(t, type = "cohens_d")
 ```
 
 *Note*, users can easily store the value and/or CIs as you'd like via, e.g., `cohens_d <- effectsize(t, type = "cohens_d")[1]`.