diff --git a/lmo/_lm.py b/lmo/_lm.py
index bbf430da..b41d17f2 100644
--- a/lmo/_lm.py
+++ b/lmo/_lm.py
@@ -357,7 +357,7 @@ def l_moment(
             - $(0, 0)$: The original **L**-moment, introduced by Hosking
                 in 1990.
             - $(0, t)$: **LL**-moment (**L**inear combination of **L**owest
-                order statistics), instroduced by Bayazit & Onoz in 2002.
+                order statistics), introduced by Bayazit & Onoz in 2002.
                 Assigns more weight to smaller observations.
             - $(s, 0)$: **LH**-moment (**L**inear combination of **H**igher
                 order statistics), as described by Wang in 1997.
diff --git a/lmo/_lm_co.py b/lmo/_lm_co.py
index 480721d3..971645c3 100644
--- a/lmo/_lm_co.py
+++ b/lmo/_lm_co.py
@@ -85,7 +85,7 @@ def l_comoment(
                 (1990).  Useful for fitting the e.g. log-normal and generalized
                 extreme value (GEV) distributions.
             - $(0, m)$: **LL**-moment (**L**inear combination of **L**owest
-                order statistics), instroduced by Bayazit & Onoz (2002).
+                order statistics), introduced by Bayazit & Onoz (2002).
                 Assigns more weight to smaller observations.
             - $(s, 0)$: **LH**-moment (**L**inear combination of **H**igher
                 order statistics), by Wang (1997).
diff --git a/lmo/linalg.py b/lmo/linalg.py
index 256d641e..9cd267d2 100644
--- a/lmo/linalg.py
+++ b/lmo/linalg.py
@@ -373,7 +373,7 @@ def trim_matrix(
     a bunch of L-moments, can be done using a single matrix multiplication
     (see [`lmo.linalg.sh_legendre`][lmo.linalg.sh_legendre]).
     By exploiting liniarity, it can easily be chained with this trim matrix,
-    to obtain a re-usable order-statistics -> trimmed L-moments
+    to obtain a reusable order-statistics -> trimmed L-moments
     transformation (matrix).
 
     Note that these linear transformations can be used in exactly the same way