diff --git a/lectures/jax_intro.md b/lectures/jax_intro.md
index 189d810..f1e5881 100644
--- a/lectures/jax_intro.md
+++ b/lectures/jax_intro.md
@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.6
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -348,7 +348,15 @@ The compiled versions for the previous array size are still available in memory
 too, and the following call is dispatched to the correct compiled code.
 
 ```{code-cell} ipython3
-%time f(x).block_until_ready()
+%timeit f(x).block_until_ready()
+```
+
+```{note}
+Note that we use the [`%timeit` magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) 
+here to get more reliable timing statistics. 
+
+It runs the code multiple times to calculate both mean and standard deviation, 
+which takes longer but provides more accurate measurements than a single run.
 ```
 
 ### Compiling the outer function
@@ -368,14 +376,13 @@ f_jit(x)
 And now let's time it.
 
 ```{code-cell} ipython3
-%time f_jit(x).block_until_ready()
+%timeit f_jit(x).block_until_ready()
 ```
 
 Note the speed gain.
 
 This is because the array operations are fused and no intermediate arrays are created.
 
-
 Incidentally, a more common syntax when targetting a function for the JIT
 compiler is