docs(readme): add examples (#48)

Author: Snoopy1866

README.md

@@ -16,25 +16,82 @@ PyStatPower 是一个专注于统计领域功效分析的开源的 Python 库。
 
 [详细文档](https://pystatpower.github.io/PyStatPower-Docs)
 
+> [!WARNING]
+> 本项目处于 alpha 阶段，文档尚未完成。
+
 ## 安装
 
 ```cmd
 pip install pystatpower
 ```
 
-## 使用
+## 示例
+
+### 计算样本量
+
+#### 单组样本率检验
+
+```python
+from pystatpower.models import one_proportion
+
+result = one_proportion.solve_for_sample_size(
+    alpha=0.05, power=0.80, nullproportion=0.80, proportion=0.95, alternative="two_sided", test_type="exact_test"
+)
+print(result)
+```
+
+输出：
+
+```python
+Size(41.59499160228066)
+```
+
+#### 两独立样本率差异性检验
+
+```python
+from pystatpower.models import two_proportion
+
+result = two_proportion.solve_for_sample_size(
+    alpha=0.05,
+    power=0.80,
+    treatment_proportion=0.95,
+    reference_proportion=0.80,
+    alternative="two_sided",
+    test_type="z_test_pooled",
+)
+print(result)
+```
+
+输出：
+
+```python
+(Size(75.11862332120842), Size(75.11862332120842))
+```
+
+### 计算检验效能
 
 ```python
-from pystatpower.procedures import ospp
+from pystatpower.models.two_proportion import *
 
-result = ospp.solve(n=None, alpha=0.05, power=0.80, nullproportion=0.80, proportion=0.95)
+result = solve_for_power(
+    alpha=0.05,
+    treatment_proportion=0.95,
+    reference_proportion=0.80,
+    alternative="two_sided",
+    test_type="z_test_pooled",
+    group_allocation=GroupAllocation.ForPower(
+        GroupAllocationOption.SIZE_OF_TREATMENT | GroupAllocationOption.SIZE_OF_REFERENCE,
+        size_of_treatment=100,
+        size_of_reference=50,
+    ),
+)
 print(result)
 ```
 
 输出：
 
 ```python
-41.594991602280594
+Power(0.7865318578853373)
 ```
 
 ## 鸣谢