diff --git a/src/plotfig/bar.py b/src/plotfig/bar.py
index a2174f5..e116166 100644
--- a/src/plotfig/bar.py
+++ b/src/plotfig/bar.py
@@ -10,6 +10,9 @@
 from matplotlib.patches import Polygon
 from scipy import stats
 
+# 设置警告过滤器，显示所有警告
+warnings.simplefilter("always")
+
 # 类型别名
 Num = int | float  # 可同时接受int和float的类型
 NumArray = list[Num] | npt.NDArray[np.float64]  # 数字数组类型
@@ -106,6 +109,7 @@ def set_yaxis(
             ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x:.0%}"))
 
 
+# 统计相关
 def perform_stat_test(
     data1: NumArray | None = None,
     data2: NumArray | None = None,
@@ -130,6 +134,29 @@ def perform_stat_test(
     return stat, p
 
 
+def determine_test_modle(data, method, p_list=None, popmean=0):
+    comparisons = []
+    idx = 0
+    if method != "ttest_1samp":
+        for i in range(len(data)):
+            for j in range(i + 1, len(data)):
+                if method == "external":
+                    p = p_list[idx]
+                    idx += 1
+                else:
+                    _, p = perform_stat_test(
+                        data1=data[i], data2=data[j], method=method
+                    )
+                if p <= 0.05:
+                    comparisons.append((i, j, p))
+    else:
+        for i in range(len(data)):
+            _, p = perform_stat_test(data1=data[i], popmean=popmean, method=method)
+            if p <= 0.05:
+                comparisons.append((i, p))
+    return comparisons
+
+
 def annotate_significance(
     ax: Axes,
     comparisons: list[tuple[int, int, float]],
@@ -168,7 +195,7 @@ def _stars(pval, i, y, color, fontsize):
             _stars(pval, (i + j) / 2, y + star_offset, color, fontsize)
     elif len(comparisons[0]) == 2:
         for i, pval in comparisons:
-            y = y_base + interval
+            y = y_base
             _stars(pval, i, y + star_offset, color, fontsize)
 
 
@@ -183,42 +210,63 @@ def statistics(
     asterisk_fontsize,
     asterisk_color,
 ):
-    comparisons = []
-    idx = 0
-    if test_method != "ttest_1samp":
-        for i in range(len(data)):
-            for j in range(i + 1, len(data)):
-                if test_method == "external":
-                    p = p_list[idx]
-                    idx += 1
-                else:
-                    _, p = perform_stat_test(
-                        data1=data[i], data2=data[j], method=test_method
-                    )
-                if p <= 0.05:
-                    comparisons.append((i, j, p))
+    if isinstance(test_method, list):
+        if len(test_method) > 2 or (
+            len(test_method) == 2 and "ttest_1samp" not in test_method
+        ):
+            raise ValueError(
+                "test_method 最多只能有2个元素。且当元素数量为2时，其中之一必须是 'ttest_1samp'。"
+            )
+
+        for method in test_method:
+            comparisons = determine_test_modle(data, method, p_list, popmean)
+            if not comparisons:
+                return
+
+            y_max = ax.get_ylim()[1]
+            interval = (y_max - np.max(all_values)) / (len(comparisons) + 1)
+
+            color = (
+                "b"
+                if len(test_method) > 1 and method == "ttest_1samp"
+                else asterisk_color
+            )
+
+            annotate_significance(
+                ax,
+                comparisons,
+                np.max(all_values),
+                interval,
+                line_color=statistical_line_color,
+                star_offset=interval / 5,
+                fontsize=asterisk_fontsize,
+                color=color,
+            )
     else:
-        for i in range(len(data)):
-            _, p = perform_stat_test(data1=data[i], popmean=popmean, method=test_method)
-            if p <= 0.05:
-                comparisons.append((i, p))
-    if not comparisons:
-        return
-
-    y_max = ax.get_ylim()[1]
-    interval = (y_max - np.max(all_values)) / (len(comparisons) + 1)
-    annotate_significance(
-        ax,
-        comparisons,
-        np.max(all_values),
-        interval,
-        line_color=statistical_line_color,
-        star_offset=interval / 5,
-        fontsize=asterisk_fontsize,
-        color=asterisk_color,
-    )
+        warnings.warn(
+            "请使用列表形式传递 test_method 参数，例如 test_method=['ttest_ind']。字符串形式 test_method='ttest_ind' 将在后续版本中弃用。",
+            DeprecationWarning,
+            stacklevel=1,
+        )
+        comparisons = determine_test_modle(data, test_method, p_list, popmean)
+        if not comparisons:
+            return
+
+        y_max = ax.get_ylim()[1]
+        interval = (y_max - np.max(all_values)) / (len(comparisons) + 1)
+        annotate_significance(
+            ax,
+            comparisons,
+            np.max(all_values),
+            interval,
+            line_color=statistical_line_color,
+            star_offset=interval / 5,
+            fontsize=asterisk_fontsize,
+            color=asterisk_color,
+        )
 
 
+# 可调用接口函数
 def plot_one_group_bar_figure(
     data: list[NumArray],
     ax: Axes | None = None,
@@ -245,8 +293,6 @@ def plot_one_group_bar_figure(
     asterisk_color: str = "k",
     **kwargs: Any,
 ) -> None:
-
-
     """绘制单组柱状图，包含散点、误差条和统计显著性标记。
 
     Args:
@@ -319,7 +365,12 @@ def plot_one_group_bar_figure(
             ax.imshow(gradient, aspect="auto", cmap=cmap, extent=extent, zorder=0)
     else:
         ax.bar(
-            x_positions, means, width=width, color=colors, alpha=color_alpha, edgecolor=edgecolor
+            x_positions,
+            means,
+            width=width,
+            color=colors,
+            alpha=color_alpha,
+            edgecolor=edgecolor,
         )
 
     ax.errorbar(
@@ -442,7 +493,9 @@ def plot_one_group_violin_figure(
     labels_name = labels_name or [str(i) for i in range(len(data))]
     colors = colors or ["gray"] * len(data)
 
-    def _draw_gradient_violin(ax, data, pos, width=width, c1="red", c2="blue", color_alpha=1):
+    def _draw_gradient_violin(
+        ax, data, pos, width=width, c1="red", c2="blue", color_alpha=1
+    ):
         # KDE估计
         kde = stats.gaussian_kde(data)
         buffer = (max(data) - min(data)) / 5
@@ -514,7 +567,9 @@ def _draw_gradient_violin(ax, data, pos, width=width, c1="red", c2="blue", color
             c2 = colors_end[i]
         else:
             c1 = c2 = colors[i]
-        ymax, ymin = _draw_gradient_violin(ax, d, pos=i, c1=c1, c2=c2, color_alpha=color_alpha)
+        ymax, ymin = _draw_gradient_violin(
+            ax, d, pos=i, c1=c1, c2=c2, color_alpha=color_alpha
+        )
         ymax_lst.append(ymax)
         ymin_lst.append(ymin)
     ymax = max(ymax_lst)