Update statistical_power.Rmd

vignettes/statistical_power.Rmd

@@ -111,8 +111,11 @@ 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}
-effectsize(t, type = "cohens_d")
+```{r warning = FALSE}
+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]`.