diff --git a/README.md b/README.md
index 02b2ec88..ef1e47b5 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,5 @@
-# DABEST-Python
-
+DABEST-Python
+================
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
diff --git a/dabest/_delta_objects.py b/dabest/_delta_objects.py
index 909aba6e..07b15ea8 100644
--- a/dabest/_delta_objects.py
+++ b/dabest/_delta_objects.py
@@ -435,17 +435,19 @@ def __init__(self, effectsizedataframe, permutation_count,
                                                  self.__control_N,
                                                  self.__test_var, 
                                                  self.__test_N)
+        
+        self.__bootstraps_variance = ci2g.calculate_bootstraps_var(self.__bootstraps)
 
         # Compute the weighted average mean differences of the bootstrap data
         # using the pooled group variances of the raw data as the inverse of 
         # weights
         self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(
-                                                          self.__group_var, 
+                                                          self.__bootstraps_variance, 
                                                           self.__bootstraps)
 
         # Compute the weighted average mean difference based on the raw data
         self.__difference = es.weighted_delta(np.array(self.__effsizedf["difference"]),
-                                                   self.__group_var)
+                                                   self.__bootstraps_variance)
 
         sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)
 
@@ -753,6 +755,14 @@ def group_var(self):
         in order. 
         '''
         return self.__group_var
+    
+    @property
+    def bootstraps_var(self):
+        '''
+        Return the variances of each bootstrapped mean difference distribution
+        in order. 
+        '''
+        return self.__bootstraps_variance
 
 
     @property
diff --git a/dabest/_modidx.py b/dabest/_modidx.py
index d51151af..7edd8f05 100644
--- a/dabest/_modidx.py
+++ b/dabest/_modidx.py
@@ -27,6 +27,8 @@
                                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.bootstrap_indices': ( 'API/confint_2group_diff.html#bootstrap_indices',
                                                                                                                         'dabest/_stats_tools/confint_2group_diff.py'),
+                                                         'dabest._stats_tools.confint_2group_diff.calculate_bootstraps_var': ( 'API/confint_2group_diff.html#calculate_bootstraps_var',
+                                                                                                                               'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_group_var': ( 'API/confint_2group_diff.html#calculate_group_var',
                                                                                                                           'dabest/_stats_tools/confint_2group_diff.py'),
                                                          'dabest._stats_tools.confint_2group_diff.calculate_weighted_delta': ( 'API/confint_2group_diff.html#calculate_weighted_delta',
diff --git a/dabest/_stats_tools/confint_2group_diff.py b/dabest/_stats_tools/confint_2group_diff.py
index 5063b8d3..1950da36 100644
--- a/dabest/_stats_tools/confint_2group_diff.py
+++ b/dabest/_stats_tools/confint_2group_diff.py
@@ -6,7 +6,7 @@
 __all__ = ['create_jackknife_indexes', 'create_repeated_indexes', 'compute_meandiff_jackknife', 'bootstrap_indices',
            'compute_bootstrapped_diff', 'delta2_bootstrap_loop', 'compute_delta2_bootstrapped_diff',
            'compute_meandiff_bias_correction', 'compute_interval_limits', 'calculate_group_var',
-           'calculate_weighted_delta']
+           'calculate_bootstraps_var', 'calculate_weighted_delta']
 
 # %% ../../nbs/API/confint_2group_diff.ipynb 4
 import numpy as np
@@ -319,15 +319,23 @@ def calculate_group_var(control_var, control_N, test_var, test_N):
     
     return pooled_var
 
+def calculate_bootstraps_var(bootstraps):
 
-def calculate_weighted_delta(group_var, differences):
+    bootstraps_var_list = [np.var(x, ddof=1) for x in bootstraps]
+    bootstraps_var_array = np.array(bootstraps_var_list)
+    
+    return bootstraps_var_array
+    
+
+
+def calculate_weighted_delta(bootstrap_dist_var, differences):
     """
     Compute the weighted deltas.
     """
 
-    weight = 1 / group_var
+    weight = np.true_divide(1, bootstrap_dist_var)
     denom = np.sum(weight)
     num = 0.0
     for i in range(len(weight)):
         num += weight[i] * differences[i]
-    return num / denom
+    return np.true_divide(num, denom)
diff --git a/dabest/_stats_tools/effsize.py b/dabest/_stats_tools/effsize.py
index 11f28d4c..2e597185 100644
--- a/dabest/_stats_tools/effsize.py
+++ b/dabest/_stats_tools/effsize.py
@@ -392,11 +392,11 @@ def _compute_hedges_correction_factor(n1,
 
 # %% ../../nbs/API/effsize.ipynb 13
 @njit(cache=True)
-def weighted_delta(difference, group_var):
+def weighted_delta(difference, bootstrap_dist_var):
     '''
     Compute the weighted deltas where the weight is the inverse of the
     pooled group difference.
     '''
 
-    weight = np.true_divide(1, group_var)
+    weight = np.true_divide(1, bootstrap_dist_var)
     return np.sum(difference*weight)/np.sum(weight)
diff --git a/nbs/API/confint_2group_diff.ipynb b/nbs/API/confint_2group_diff.ipynb
index 29ca48ae..bdc009b3 100644
--- a/nbs/API/confint_2group_diff.ipynb
+++ b/nbs/API/confint_2group_diff.ipynb
@@ -373,18 +373,26 @@
     "    \n",
     "    return pooled_var\n",
     "\n",
+    "def calculate_bootstraps_var(bootstraps):\n",
     "\n",
-    "def calculate_weighted_delta(group_var, differences):\n",
+    "    bootstraps_var_list = [np.var(x, ddof=1) for x in bootstraps]\n",
+    "    bootstraps_var_array = np.array(bootstraps_var_list)\n",
+    "    \n",
+    "    return bootstraps_var_array\n",
+    "    \n",
+    "\n",
+    "\n",
+    "def calculate_weighted_delta(bootstrap_dist_var, differences):\n",
     "    \"\"\"\n",
     "    Compute the weighted deltas.\n",
     "    \"\"\"\n",
     "\n",
-    "    weight = 1 / group_var\n",
+    "    weight = np.true_divide(1, bootstrap_dist_var)\n",
     "    denom = np.sum(weight)\n",
     "    num = 0.0\n",
     "    for i in range(len(weight)):\n",
     "        num += weight[i] * differences[i]\n",
-    "    return num / denom"
+    "    return np.true_divide(num, denom)"
    ]
   }
  ],
diff --git a/nbs/API/delta_objects.ipynb b/nbs/API/delta_objects.ipynb
index bae2ffca..ca09104f 100644
--- a/nbs/API/delta_objects.ipynb
+++ b/nbs/API/delta_objects.ipynb
@@ -46,7 +46,44 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|███████████████████████████████████████████████████████| 11/11 [01:01<00:00,  5.55s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "#| hide\n",
     "import dabest"
@@ -469,11 +506,23 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n",
+      "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 5.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n",
+      "C:\\Users\\maiyi\\anaconda3\\Lib\\site-packages\\dabest\\plot_tools.py:2537: UserWarning: 20.0% of the points cannot be placed. You might want to decrease the size of the markers.\n",
+      "  warnings.warn(err)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 900x600 with 3 Axes>"
+       "<Figure size 750x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -575,17 +624,19 @@
     "                                                 self.__control_N,\n",
     "                                                 self.__test_var, \n",
     "                                                 self.__test_N)\n",
+    "        \n",
+    "        self.__bootstraps_variance = ci2g.calculate_bootstraps_var(self.__bootstraps)\n",
     "\n",
     "        # Compute the weighted average mean differences of the bootstrap data\n",
     "        # using the pooled group variances of the raw data as the inverse of \n",
     "        # weights\n",
     "        self.__bootstraps_weighted_delta = ci2g.calculate_weighted_delta(\n",
-    "                                                          self.__group_var, \n",
+    "                                                          self.__bootstraps_variance, \n",
     "                                                          self.__bootstraps)\n",
     "\n",
     "        # Compute the weighted average mean difference based on the raw data\n",
     "        self.__difference = es.weighted_delta(np.array(self.__effsizedf[\"difference\"]),\n",
-    "                                                   self.__group_var)\n",
+    "                                                   self.__bootstraps_variance)\n",
     "\n",
     "        sorted_weighted_deltas = npsort(self.__bootstraps_weighted_delta)\n",
     "\n",
@@ -893,6 +944,14 @@
     "        in order. \n",
     "        '''\n",
     "        return self.__group_var\n",
+    "    \n",
+    "    @property\n",
+    "    def bootstraps_var(self):\n",
+    "        '''\n",
+    "        Return the variances of each bootstrapped mean difference distribution\n",
+    "        in order. \n",
+    "        '''\n",
+    "        return self.__bootstraps_variance\n",
     "\n",
     "\n",
     "    @property\n",
@@ -1036,13 +1095,13 @@
     {
      "data": {
       "text/plain": [
-       "DABEST v2024.03.29\n",
+       "DABEST v2025.03.27\n",
        "==================\n",
        "                  \n",
        "Good afternoon!\n",
-       "The current time is Tue Mar 19 15:34:33 2024.\n",
+       "The current time is Mon Sep  1 16:03:47 2025.\n",
        "\n",
-       "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.137, 0.228].\n",
+       "The weighted-average unpaired mean differences is 0.0336 [95%CI -0.136, 0.236].\n",
        "The p-value of the two-sided permutation t-test is 0.736, calculated for legacy purposes only. \n",
        "\n",
        "5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.\n",
@@ -1098,5 +1157,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/nbs/API/effsize.ipynb b/nbs/API/effsize.ipynb
index 65259d81..475b38a9 100644
--- a/nbs/API/effsize.ipynb
+++ b/nbs/API/effsize.ipynb
@@ -507,13 +507,13 @@
    "source": [
     "#| export\n",
     "@njit(cache=True)\n",
-    "def weighted_delta(difference, group_var):\n",
+    "def weighted_delta(difference, bootstrap_dist_var):\n",
     "    '''\n",
     "    Compute the weighted deltas where the weight is the inverse of the\n",
     "    pooled group difference.\n",
     "    '''\n",
     "\n",
-    "    weight = np.true_divide(1, group_var)\n",
+    "    weight = np.true_divide(1, bootstrap_dist_var)\n",
     "    return np.sum(difference*weight)/np.sum(weight)"
    ]
   }
diff --git a/nbs/API/effsize_objects.ipynb b/nbs/API/effsize_objects.ipynb
index cea66708..4da0136f 100644
--- a/nbs/API/effsize_objects.ipynb
+++ b/nbs/API/effsize_objects.ipynb
@@ -2403,5 +2403,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/nbs/tests/mpl_image_tests/baseline_images/test_502_minimeta_forest.png b/nbs/tests/mpl_image_tests/baseline_images/test_502_minimeta_forest.png
index 93407885..b9b2bc34 100644
Binary files a/nbs/tests/mpl_image_tests/baseline_images/test_502_minimeta_forest.png and b/nbs/tests/mpl_image_tests/baseline_images/test_502_minimeta_forest.png differ
diff --git a/nbs/tests/test_08_mini_meta_pvals.ipynb b/nbs/tests/test_08_mini_meta_pvals.ipynb
index 0c38d9b3..98a40f23 100644
--- a/nbs/tests/test_08_mini_meta_pvals.ipynb
+++ b/nbs/tests/test_08_mini_meta_pvals.ipynb
@@ -16,7 +16,36 @@
    "execution_count": null,
    "id": "90ea3a40",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-compiling numba functions for DABEST...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Compiling numba functions: 100%|███████████████████████████████████████████████████████| 11/11 [00:01<00:00,  7.62it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Numba compilation complete!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "from dabest._stats_tools import effsize\n",
     "from dabest._stats_tools import confint_2group_diff as ci2g\n",
@@ -38,6 +67,27 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6972edf3-87e0-4ab2-88d6-0726a2e6e0d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.51539707, 10.22387374])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unpaired.mean_diff.mini_meta.bootstraps_var"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "86994f88",
@@ -64,7 +114,7 @@
    "id": "7cf4d56d",
    "metadata": {},
    "source": [
-    "test_variances"
+    "test_pooled_variances"
    ]
   },
   {
@@ -93,6 +143,29 @@
     "assert group_var == pytest.approx(np_group_var)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e06ceb8e-4f54-4ba5-9703-42089e1b6b86",
+   "metadata": {},
+   "source": [
+    "test_bootstrap_distribution_variances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88931a0f-a9cb-4e16-8cc3-c7c5be282171",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bootstrap_distributions = unpaired.mean_diff.mini_meta.bootstraps\n",
+    "bootstrap_distribution_variances = unpaired.mean_diff.mini_meta.bootstraps_var\n",
+    "\n",
+    "np_bootstrap_distribution_variances = np.array([np.var(x, ddof=1) for x in bootstrap_distributions])\n",
+    "\n",
+    "assert bootstrap_distribution_variances == pytest.approx(np_bootstrap_distribution_variances)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a2c934e5",
@@ -112,12 +185,37 @@
     "\n",
     "np_means = np.array([np.mean(rep1_yes)-np.mean(rep1_no), \n",
     "            np.mean(rep2_yes)-np.mean(rep2_no)])\n",
-    "np_var   = np.array([np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n",
-    "            np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N])\n",
+    "\n",
+    "np_var   = np_bootstrap_distribution_variances\n",
     "\n",
     "np_difference = effsize.weighted_delta(np_means, np_var)\n",
     "\n",
-    "assert difference == pytest.approx(np_difference)"
+    "weight = np.true_divide(1, np_var)\n",
+    "np_difference_calc = np.sum(np_means*weight)/np.sum(weight)\n",
+    "\n",
+    "assert difference == pytest.approx(np_difference) == pytest.approx(np_difference_calc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b9e81da-01f9-4880-acde-0dd9dd6caf12",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-1.32919358,  1.17274469,  0.51495794, ...,  0.20620551,\n",
+       "       -2.86746452,  2.19964192])"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mini_meta_delta.permutations_weighted_delta"
    ]
   },
   {
@@ -131,9 +229,20 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "45056c5f",
+   "id": "d674181c-82c1-4116-804a-69e9def7d5c8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0094"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mini_meta_delta   = unpaired.mean_diff.mini_meta\n",
     "pvalue             = mini_meta_delta.pvalue_permutation\n",
@@ -150,6 +259,8 @@
     "permutations_1_var = perm_test_1.permutations_var\n",
     "permutations_2_var = perm_test_2.permutations_var\n",
     "\n",
+    "perm_test_1\n",
+    "\n",
     "weight_1 = np.true_divide(1,permutations_1_var)\n",
     "weight_2 = np.true_divide(1,permutations_2_var)\n",
     "\n",
@@ -157,19 +268,48 @@
     "assert permutations_delta == pytest.approx(weighted_deltas)\n",
     "\n",
     "\n",
-    "np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n",
-    "            np.mean(rep2_yes)-np.mean(rep2_no)]\n",
-    "np_var   = [np.var(rep1_yes, ddof=1)/N+np.var(rep1_no, ddof=1)/N,\n",
-    "            np.var(rep2_yes, ddof=1)/N+np.var(rep2_no, ddof=1)/N]\n",
-    "np_weight= np.true_divide(1, np_var)\n",
+    "# np_means = [np.mean(rep1_yes)-np.mean(rep1_no), \n",
+    "#             np.mean(rep2_yes)-np.mean(rep2_no)]\n",
+    "# np_var   = [np.var(rep1_yes, ddof=1)/(N-1)+np.var(rep1_no, ddof=1)/(N-1),\n",
+    "#             np.var(rep2_yes, ddof=1)/(N-1)+np.var(rep2_no, ddof=1)/(N-1)]\n",
     "\n",
-    "np_difference = np.sum(np_means*np_weight)/np.sum(np_weight)\n",
+    "# np_weight= np.true_divide(1, np_var)\n",
     "\n",
-    "np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_difference), \n",
-    "                            weighted_deltas)))/len(weighted_deltas)\n",
+    "# np_difference = np.sum(np_means*np_weight)/np.sum(np_weight)\n",
     "\n",
-    "assert pvalue == pytest.approx(np_pvalues)"
+    "# np_pvalues = len(list(filter(lambda x: np.abs(x)>np.abs(np_difference), \n",
+    "#                             weighted_deltas)))/len(weighted_deltas)\n",
+    "\n",
+    "# assert pvalue == pytest.approx(np_pvalues)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db42467a-0e0c-463e-be4a-a0a31f22db60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.abs(np_difference)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c20cc0f-5b4e-4d24-9617-c346a3b5daa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvalue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e3bb0bd-b49f-48b0-98a9-fef495cb27a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..244792ad
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,11 @@
+[build-system]
+requires = ["setuptools>=64.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name="dabest"
+requires-python=">=3.10"
+dynamic = [ "keywords", "description", "version", "dependencies", "optional-dependencies", "readme", "license", "authors", "classifiers", "entry-points", "scripts", "urls"]
+
+[tool.uv]
+cache-keys = [{ file = "pyproject.toml" }, { file = "settings.ini" }, { file = "setup.py" }]