From 873eb4b1e7b608bc692bc02083b8a5319742d8c9 Mon Sep 17 00:00:00 2001
From: Matias Laporte <matiaslaporte@gmail.com>
Date: Thu, 17 Oct 2024 07:15:24 +0200
Subject: [PATCH] Update parametric.py - fix typo (#448)

---
 src/pingouin/parametric.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/pingouin/parametric.py b/src/pingouin/parametric.py
index f8f87c9f..e97063bb 100644
--- a/src/pingouin/parametric.py
+++ b/src/pingouin/parametric.py
@@ -1262,7 +1262,7 @@ def welch_anova(data=None, dv=None, between=None):
     it is best to use the Welch ANOVA that better controls for
     type I error (Liu 2015). The homogeneity of variances can be measured with
     the `homoscedasticity` function. The two other assumptions of
-    normality and independance remain.
+    normality and independence remain.
 
     The main idea of Welch ANOVA is to use a weight :math:`w_i` to reduce
     the effect of unequal variances. This weight is calculated using the sample