diff --git a/source/statistics/figures/normal_law_tails.svg b/source/statistics/figures/normal_law_tails.svg
new file mode 100644
index 0000000..13f05c4
--- /dev/null
+++ b/source/statistics/figures/normal_law_tails.svg
@@ -0,0 +1,416 @@
+
+
+
+
diff --git a/source/statistics/tests/parametric/ztest.rst b/source/statistics/tests/parametric/ztest.rst
index 82c3586..20dc042 100644
--- a/source/statistics/tests/parametric/ztest.rst
+++ b/source/statistics/tests/parametric/ztest.rst
@@ -2,7 +2,7 @@ Z-Test
-------
The z-test is used to assess if the mean :math:`\overline{x}` of sample :math:`X` significantly differ from the one of a known population.
-The *significance level* is determined by a *p-value* threshold.
+The *significance level* is determined by a *p-value* threshold chosen prior doing the test.
Conditions for using a z-test:
@@ -14,12 +14,10 @@ Conditions for using a z-test:
According to central limit theorem, a distribution is well approximated when reaching 30 samples.
See `here `__ for more infos.
-One-tailed vs Two-tailed
-========================
To perform a z-test, you should compute the *standard score* (or *z-score*) of your sample.
-It corresponds to the projection of the sample mean :math:`\overline{x}` under the original population distribution.
+It characterizes how far from the population mean :math:`\mu` your sample mean :math:`\overline{x}` is, in unit of standard deviation :math:`\sigma`.
It is computed as follow:
.. math::
@@ -31,10 +29,34 @@ It is computed as follow:
.. math::
Z=\frac{\overline{x}-\mu}{\mathrm{SEM}}=\frac{\overline{x}-\mu}{\frac{s}{\sqrt{n}}}
- This formula originate from the t-test and :math:`Z` technically follow a t-distribution.
- However, if :math:`n` is sufficiently large, the sample distribution is very close to a normal one.
- So close that, using the normal in place of the student-t to compute p values leads to nominal differences (`source `__).
-
+ In this case, :math:`Z` technically follow a t-distribution (student test).
+ However, if :math:`n` is sufficiently large, the distribution followed by :math:`Z` is very close to a normal one.
+ So close that, using z-test in place of the student test to compute *p-values* leads to nominal differences (`source `__).
+
+From :math:`Z`, the z-test *p-value* can be derived using the :math:`\mathcal{N}(0,1)` :ref:`CDF `.
+That *p-value* is computed as follow:
+
+* Left "tail" of the :math:`\mathcal{N}(0,1)` distribution:
+
+ .. math::
+ \alpha=P(\mathcal{N}(0,1)