diff --git a/doc/source/intro_robuststats.md b/doc/source/intro_robuststats.md
index f8e58f3c..e410c5e1 100644
--- a/doc/source/intro_robuststats.md
+++ b/doc/source/intro_robuststats.md
@@ -30,14 +30,14 @@ used as a robust measure of central tendency.
 
 The median is used by default in the alignment routines of {ref}`coregistration` and {ref}`biascorr`.
 
+(robuststats-nmad)=
+
 ### Dispersion
 
 The [statistical dispersion](https://en.wikipedia.org/wiki/Statistical_dispersion) represents the spread of a sample,
 and is core to the analysis of sample precision (see {ref}`intro`). It is typically measured by the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation).
 However, very much like the mean, the standard deviation is a measure sensitive to outliers.
 
-(robuststats-nmad)=
-
 The median equivalent of a standard deviation is the normalized median absolute deviation (NMAD), which corresponds to the [median absolute deviation](https://en.wikipedia.org/wiki/Median_absolute_deviation) scaled by a factor of ~1.4826 to match the dispersion of a
 normal distribution. It has been shown to provide more robust measures of dispersion with outliers when working
 with DEMs (e.g., [Höhle and Höhle (2009)](https://doi.org/10.1016/j.isprsjprs.2009.02.003)).
diff --git a/examples/advanced/plot_standardization.py b/examples/advanced/plot_standardization.py
index 34fbe26d..b0128f34 100644
--- a/examples/advanced/plot_standardization.py
+++ b/examples/advanced/plot_standardization.py
@@ -99,7 +99,7 @@
 
 # %%
 # We perform a scale-correction for the standardization, to ensure that the spread of the data is exactly 1.
-# The NMAD is used as a robust measure for the spread (see :ref:`robuststats-nmad`).
+# The NMAD is used as a robust measure for the spread (see :ref:`robuststats-nmad`). 
 print(f"NMAD before scale-correction: {nmad(z_dh.data):.1f}")
 scale_fac_std = nmad(z_dh.data)
 z_dh = z_dh / scale_fac_std