From 537fe67bef66abe563c014176bf275d4f077cd73 Mon Sep 17 00:00:00 2001
From: Philip Waggoner <31326382+pdwaggoner@users.noreply.github.com>
Date: Tue, 19 Sep 2023 10:43:21 -0600
Subject: [PATCH] Update statistical_power.Rmd

---
 vignettes/statistical_power.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/vignettes/statistical_power.Rmd b/vignettes/statistical_power.Rmd
index 1aba1288..b433d18e 100644
--- a/vignettes/statistical_power.Rmd
+++ b/vignettes/statistical_power.Rmd
@@ -111,7 +111,7 @@ 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}
+```{r eval = FALSE}
 effectsize(t, type = "cohens_d")
 ```