From 40c0956a3e9473c5baed565226ea9f1a3a534f83 Mon Sep 17 00:00:00 2001
From: Philip Waggoner <31326382+pdwaggoner@users.noreply.github.com>
Date: Tue, 19 Sep 2023 08:47:08 -0600
Subject: [PATCH] Update standardized_differences.Rmd

reverting two small changes back to fix lint issues
---
 vignettes/standardized_differences.Rmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/standardized_differences.Rmd b/vignettes/standardized_differences.Rmd
index ef815526..a1aa33e1 100644
--- a/vignettes/standardized_differences.Rmd
+++ b/vignettes/standardized_differences.Rmd
@@ -47,8 +47,8 @@ is larger (and vice versa).
 ## Two Independent Samples
 
 For two independent samples, the difference between the means is standardized
-based on the pooled standard deviation of both samples, which are assumed to be equal in
-the population:
+based on the pooled standard deviation of both samples (assumed to be equal in
+the population):
 
 ```{r}
 t.test(mpg ~ am, data = mtcars, var.equal = TRUE)
@@ -222,7 +222,7 @@ cohens_u1(mpg ~ am, data = mtcars)
 p_overlap(mpg ~ am, data = mtcars)
 ```
 
-Note that by default these functions return the parametric versions of these effect sizes: these assume equal normal variance in both populations. When these assumptions are not met, the values produced will be biased in unknown ways. In such cases, we should use the non-parametric versions ($U_1$ is not defined):
+Note the by default, these functions return the parametric versions of these effect sizes: these assume equal normal variance in both populations. When these assumptions are not met, the values produced will be biased in unknown ways. In such cases, we should use the non-parametric versions ($U_1$ is not defined):
 
 ```{r}
 p_overlap(mpg ~ am, data = mtcars, parametric = FALSE)