diff --git a/source/statistics/index.rst b/source/statistics/index.rst
index 75988cb..ee3e426 100644
--- a/source/statistics/index.rst
+++ b/source/statistics/index.rst
@@ -2,10 +2,9 @@ Statistics
 =========================
 
 .. toctree::
-   :maxdepth: 2
    :numbered:
+   :maxdepth: 2
 
-   introduction
+   metrics
 
-dlkdd
-sd
+Statistics notes.
diff --git a/source/statistics/introduction.rst b/source/statistics/metrics.rst
similarity index 93%
rename from source/statistics/introduction.rst
rename to source/statistics/metrics.rst
index f1f7691..b178e51 100644
--- a/source/statistics/introduction.rst
+++ b/source/statistics/metrics.rst
@@ -1,14 +1,11 @@
-Introduction
-==================
-
 Metrics
-----------------
+==================
 
 * **Expected value/Espérance**: Noted :math:`\mathbb{E}[X]`, is a **theorical value**. For example, when playing coin
   flipping, the expected value for getting heads or tails is 0.5.
 
 Variance
-^^^^^^^^^^^^^^
+------------------
 
 Variance can be seen as the expected squared deviation from the expected value of a random variable :math:`X`.
 
@@ -16,7 +13,7 @@ Variance can be seen as the expected squared deviation from the expected value o
    \mathbb{V}[X]=\mathbb{E}[X-\mathbb{E}[X]]^2=\frac{\sum_{i=1}^n (x_i - \mathbb{E}[X])^2}{n}=\mathrm{Cov}(X,X)
 
 Covariance
-^^^^^^^^^^^^^^
+------------------
 
 Covariance is a way to quantify the relationship between two random variables :math:`X` and
 :math:`Y` (`source <https://www.youtube.com/watch?v=qtaqvPAeEJY>`_). Covariance **DOES NOT**
@@ -33,7 +30,7 @@ Null
    \mathrm{Cov}(X,Y)=\mathbb{E}[(X-\mathbb{E}[X])(Y-\mathbb{E}[Y])]=\frac{\sum_{i=1}^n (x_i - \mathbb{E}[X])(y_i - \mathbb{E}[Y])}{n}
     
 Standard deviation
-^^^^^^^^^^^^^^^^^^^^^
+-----------------------
 
 Standard deviation provides a way to interprete the variance using the unit of :math:`X`.
 
@@ -42,7 +39,7 @@ Standard deviation provides a way to interprete the variance using the unit of :
 
 
 Standard Error of the Mean
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-----------------------------
 
 Standard Error of the Mean (SEM) quantifies the error that is potentially made when computing the mean.
 
@@ -72,7 +69,3 @@ Output example:
    ----- Experiment 3 -----
    Means SD: 1.27
    SEM       1.26
-
-
-Degree of freedom
--------------------