diff --git a/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex b/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
index 922b6c6..16f352c 100644
--- a/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
+++ b/semester4/ps/ps-jh/parts/05_limit-theorems/00_intro.tex
@@ -4,4 +4,4 @@
 
 \shortremark \hl{Vereinfacht}: Beide besagen, dass $\limit{n}{\8} \overline{\cX}_n \approx \E[\cX_k]$
 
-% TODO: Possibly add the remark from P288 (P6 in Slide Deck 6) on expected value
+\shortremark Erwartungswert und Varianz mit lin, etc berechnen
diff --git a/semester4/ps/ps-jh/parts/06_estimators/03_properties.tex b/semester4/ps/ps-jh/parts/06_estimators/03_properties.tex
index 5776b5c..0193521 100644
--- a/semester4/ps/ps-jh/parts/06_estimators/03_properties.tex
+++ b/semester4/ps/ps-jh/parts/06_estimators/03_properties.tex
@@ -16,6 +16,8 @@ Dabei $T \sim \cN(n \E_\vartheta[\cY_k], n \V_\vartheta[\cY_k])$ unter $\P_\vart
 \shortremark $\cX_k \sim \cN(0, 1)$ i.i.d: $\displaystyle \left( \sum_{k = 1}^{m} \cX_k^2 \right) \sim \chi^2_m$.
 $\chi^2_2 = \text{Exp}(\frac{1}{2})$
 
+\shortremark Allgemein: $\cX_k \sim \cN(\mu, \sigma^2)$, dann $\frac{n - 1}{\sigma^2} S^2 \sim \chi_{n - 1}^2$, mit $S$ eine Summe wie oberhalb
+
 \shortdefinition[Studentsche $t$-Verteilung] $\cX \sim t_m$ falls Dichte
 \[
     f_\cX(x) = \frac{\Gamma\left( \frac{m + 1}{2} \right)}{\sqrt{m \pi} \Gamma \left( \frac{m}{2} \right)} \left( 1 + \frac{x^2}{m} \right)^{-\frac{m + 1}{2}}
diff --git a/semester4/ps/ps-jh/parts/07_tests/02_examples.tex b/semester4/ps/ps-jh/parts/07_tests/02_examples.tex
index 3c0fa8e..fe4a873 100644
--- a/semester4/ps/ps-jh/parts/07_tests/02_examples.tex
+++ b/semester4/ps/ps-jh/parts/07_tests/02_examples.tex
@@ -14,9 +14,11 @@
         \item Testentscheid: Basierend auf Resultat von vorherigem und $K$ entscheiden.
     \end{enumerate}
 }
-\bi{Verwerfungsbereich} $K_l = (c_>, \8)$ (links), $K_r = (-\8, c_<)$ (rechts), $K_b = (-\8, -c_{\neq}) \cup (c_{\neq}, \8)$ (beidseitig)
+\bi{Approximative Teststatistik} Normalisierung unter $H_0$, also $T = \frac{x_n - \E[S_n]}{\V[S_n]}$, mit $x_n$ Realisierung, $S_n$ wie immer
 
 \newpage
+\bi{Verwerfungsbereich} $K_l = (c_>, \8)$ (links), $K_r = (-\8, c_<)$ (rechts), $K_b = (-\8, -c_{\neq}) \cup (c_{\neq}, \8)$ (beidseitig)
+
 \shortexample[Gauss-Test] Voraussetz.:
 \bi{(1)} $\cX_i$ i.i.d. $\sim \cN(\vartheta, \sigma^2)$ unter $\P_\vartheta$
 \bi{(2)} $\sigma^2$ bekannt.
diff --git a/semester4/ps/ps-jh/parts/10_tips-and-tricks/01_various.tex b/semester4/ps/ps-jh/parts/10_tips-and-tricks/01_various.tex
index d4f2357..af8a0f4 100644
--- a/semester4/ps/ps-jh/parts/10_tips-and-tricks/01_various.tex
+++ b/semester4/ps/ps-jh/parts/10_tips-and-tricks/01_various.tex
@@ -1,4 +1,4 @@
-\rmvspace
+\newpage
 \subsection{Verschiedene Funktionen}
 \shortremark[Ungleichungen] Zur Erinnerung: Richtung wechselt bei Muliplikation mit (Division durch) negative Zahl.
 
diff --git a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf
index f281233..c87a45a 100644
Binary files a/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf and b/semester4/ps/ps-jh/probability-and-statistics-cheatsheet.pdf differ