diff --git a/docs/theory.md b/docs/theory.md
new file mode 100644
index 00000000..9c9b6683
--- /dev/null
+++ b/docs/theory.md
@@ -0,0 +1,25 @@
+## 大数定律
+
+当样本量 $n \Rightarrow \infty$ 时，样本比例 $\hat{p}$ 依概率收敛于总体比例 $p$，即
+
+$$
+\hat{p} \xrightarrow{P} p
+$$
+
+## 连续映射定理
+
+若 $g$ 是连续函数，且 $X_n \xrightarrow{P} X$，则 $g(X_n) \xrightarrow{P} g(X)$
+
+## 正态分布的性质
+
+1. 若 $X \sim N(\mu, \sigma^2)$，则 $P(X \le x) = \Phi(\frac{x-\mu}{\sigma})$
+2. 若 $X \sim N(\mu, \sigma^2)$，则 $P(X \ge x) = 1 - \Phi(\frac{x-\mu}{\sigma})$
+3. $\Phi(x) = 1 - \Phi(-x)$
+
+## Slutsky 定理
+
+令 $X_n \xrightarrow{d} X$ 且 $Y_n \xrightarrow{P} c$，其中 $c$ 为常数，则：
+
+1. $X_n + Y_n \xrightarrow{d} X + c$
+2. $X_nY_n \xrightarrow{d} cX$
+3. $Y_n^{-1}X_n \xrightarrow{d} c^{-1}X$，其中 $c \neq 0$
diff --git a/zensical.toml b/zensical.toml
index 8bb3d6a9..020e5b5f 100644
--- a/zensical.toml
+++ b/zensical.toml
@@ -8,7 +8,8 @@ nav = [
         { "两独立样本率优效性设计" = "models/proportion/independent/superiority.md" },
         { "相关系数" = "models/correlation/inequality.md" }
     ] },
-    { "API Reference" = "api.md" }
+    { "API Reference" = "api.md" },
+    { "Theory Reference" = "theory.md" }
 ]
 site_name = "pystatpower"
 site_url = "https://pystatpower.readthedocs.io"