diff --git a/lectures/_config.yml b/lectures/_config.yml
index e7ec9b45..58c8263f 100644
--- a/lectures/_config.yml
+++ b/lectures/_config.yml
@@ -146,6 +146,7 @@ sphinx:
     rediraffe_redirects:
       index_toc.md: intro.md
       lake_model.md: lake_model_intro.md
+      lln_clt.md: lln_clt_intro.md
     # Remote Redirects
     redirects:
       ak2:   https://python.quantecon.org/ak2.html
diff --git a/lectures/_toc.yml b/lectures/_toc.yml
index 0d898865..5c589e6b 100644
--- a/lectures/_toc.yml
+++ b/lectures/_toc.yml
@@ -19,7 +19,7 @@ parts:
   - file: observed_distributions
   - file: fitting_distributions
   - file: bivariate_dist
-  - file: lln_clt
+  - file: lln_clt_intro
   - file: heavy_tails
 - caption: Estimation
   numbered: true
diff --git a/lectures/heavy_tails.md b/lectures/heavy_tails.md
index 0aa8a450..f6bb8143 100644
--- a/lectures/heavy_tails.md
+++ b/lectures/heavy_tails.md
@@ -952,7 +952,7 @@ Averaging tends to eliminate extreme outcomes.
 One impact of heavy tails is that sample averages can be poor estimators of
 the underlying mean of the distribution.
 
-To understand this point better, recall {doc}`our earlier discussion <lln_clt>` 
+To understand this point better, recall {doc}`our earlier discussion <lln_clt_intro>` 
 of the law of large numbers, which considered IID $X_1, \ldots, X_n$ with common distribution $F$
 
 If $\mathbb E |X_i|$ is finite, then
diff --git a/lectures/lln_clt.md b/lectures/lln_clt_intro.md
similarity index 100%
rename from lectures/lln_clt.md
rename to lectures/lln_clt_intro.md
diff --git a/lectures/markov_chains_II.md b/lectures/markov_chains_II.md
index e6da714e..88e62eb9 100644
--- a/lectures/markov_chains_II.md
+++ b/lectures/markov_chains_II.md
@@ -196,7 +196,7 @@ This gives us another way to interpret the stationary distribution (provided irr
 
 Importantly, the result is valid for any choice of $\psi_0$.
 
-The theorem is related to {doc}`the law of large numbers <lln_clt>`.
+The theorem is related to {doc}`the law of large numbers <lln_clt_intro>`.
 
 It tells us that, in some settings, the law of large numbers sometimes holds even when the
 sequence of random variables is [not IID](iid_violation).
diff --git a/lectures/monte_carlo.md b/lectures/monte_carlo.md
index 0a9ec14e..bc70c2ea 100644
--- a/lectures/monte_carlo.md
+++ b/lectures/monte_carlo.md
@@ -140,7 +140,7 @@ This is the Monte Carlo method, which runs as follows:
 
 This average will be close to the true mean when $n$ is large.
 
-This is due to the law of large numbers, which we discussed in {doc}`lln_clt`.
+This is due to the law of large numbers, which we discussed in {doc}`lln_clt_intro`.
 
 We use the following values for $p$ and each $\mu_i$ and $\sigma_i$.
 
diff --git a/lectures/observed_distributions.md b/lectures/observed_distributions.md
index 88019ea8..05c943e0 100644
--- a/lectures/observed_distributions.md
+++ b/lectures/observed_distributions.md
@@ -909,7 +909,7 @@ print(f'{"":16}population mean = {u.mean():.4f}')
 ```
 
 This convergence is a version of the *law of large numbers*, which we discuss
-in {doc}`lln_clt`.
+in {doc}`lln_clt_intro`.
 
 
 ### The role of independence
@@ -973,5 +973,5 @@ What matters is that new observations keep bringing new information, which the
 example above destroys entirely.
 
 The general question of what a sample can tell us about its distribution is
-taken up in {doc}`lln_clt`.
+taken up in {doc}`lln_clt_intro`.
 ```