diff --git a/Practical_c.Rmd b/Practical_c.Rmd
index 8c3ab9d9c2e7b53da164c60ad978f512ecfba1f8..c1116eb170b1c30b1329267d1a7c12179c40a2bc 100644
--- a/Practical_c.Rmd
+++ b/Practical_c.Rmd
@@ -310,11 +310,11 @@ What are type I and type II risks?
 <details><summary>Solution</summary>
 <p>
 
-- Type I risk: $\alpha = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is true}$
+- Type I risk: $\alpha = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is true})$
 
-- Type II risk: $\beta = \PP(\text{not reject } \H_0\ \vert\ \H_0 \text{ is false}$
+- Type II risk: $\beta = \PP(\text{not reject } \H_0\ \vert\ \H_0 \text{ is false})$
 
-- Power $= 1 - \beta = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is false}$
+- Power $= 1 - \beta = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is false})$
 
 ---
 
@@ -614,7 +614,7 @@ Imagine a procedure based on simulation to estimate the power of the previous T-
 <details><summary>Solution</summary>
 <p>
 
-The power of a test is $1 - \beta = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is false}$ where $\beta = \PP(\text{not reject } \H_0\ \vert\ \H_0 \text{ is false}$ is the type II risk.
+The power of a test is $1 - \beta = \PP(\text{reject } \H_0\ \vert\ \H_0 \text{ is false})$ where $\beta = \PP(\text{not reject } \H_0\ \vert\ \H_0 \text{ is false})$ is the type II risk.
 
 To estimate $\beta$, we need to repeat the same experiment multiple time and estimate the corresponding probability.
 
@@ -690,7 +690,7 @@ Decreasing $\alpha$ to reduce the type I error decreases the power of the test.
 **Important:**
 
 - confirm a detected effect with additional experiments/studies
-- the more (independent) studies, the lower risk of incorrect conclusion
+- the more (independent) studies, the lower risk of incorrect conclusions
 
 ---
 
@@ -1749,7 +1749,7 @@ ggplot(test_result) + geom_line(aes(x=p_values, y=fdr_adj_p_values)) +
 </p>
 </details>
 
-Eventually, we can also compare the number of significant SNPs found before and after p-value correction depending on the chosen type I risk alpha.
+Eventually, we can also compare the number of significant SNPs found before and after p-value correction depending on the chosen type I risk $\alpha$.
 
 ```{r}
 # compute the number of significant SNPs
@@ -1839,4 +1839,4 @@ Write me!
 
 ---
 
-[The .Rmd file corresponding to this page is available here under the AGPL3 Licence](https://lbmc.gitbiopages.ens-lyon.fr/hub/formations/ens_m1_ml/Practical_b.Rmd)
+[The .Rmd file corresponding to this page is available here under the AGPL3 Licence](https://lbmc.gitbiopages.ens-lyon.fr/hub/formations/ens_m1_ml/Practical_c.Rmd)