diff --git a/semester2/algorithms-and-probability/aw-summary.pdf b/semester2/algorithms-and-probability/aw-summary.pdf
index 880c124..1f94488 100644
Binary files a/semester2/algorithms-and-probability/aw-summary.pdf and b/semester2/algorithms-and-probability/aw-summary.pdf differ
diff --git a/semester2/algorithms-and-probability/parts/probability/approximating-probabilities.tex b/semester2/algorithms-and-probability/parts/probability/approximating-probabilities.tex
index b8a1099..10e3612 100644
--- a/semester2/algorithms-and-probability/parts/probability/approximating-probabilities.tex
+++ b/semester2/algorithms-and-probability/parts/probability/approximating-probabilities.tex
@@ -15,7 +15,7 @@ Markov's inequality is fairly straight forward to prove, and it already allows u
 \begin{theorem}[]{Chebyshev's inequality}
     Let $\mathcal{X}$ be a random variable and $t > 0 \in\R$. Then we have
     \begin{align*}
-        \Pr[|\mathcal{X} - \E[\mathcal{X}| \geq t]] \leq \frac{\text{Var}[\mathcal{X}]}{t^2} \Longleftrightarrow \Pr[|\mathcal{X} - \E[\mathcal{X}]| \geq t \cdot \sqrt{\text{Var}[\mathcal{X}]}] \leq \frac{1}{t^2}
+        \Pr[|\mathcal{X} - \E[\mathcal{X}]| \geq t] \leq \frac{\text{Var}[\mathcal{X}]}{t^2} \Longleftrightarrow \Pr[|\mathcal{X} - \E[\mathcal{X}]| \geq t \cdot \sqrt{\text{Var}[\mathcal{X}]}] \leq \frac{1}{t^2}
     \end{align*}
 \end{theorem}