diff --git a/docs/custom-metrics.md b/docs/custom-metrics.md
index f252e2d..2328ed1 100644
--- a/docs/custom-metrics.md
+++ b/docs/custom-metrics.md
@@ -45,7 +45,7 @@ user: [[0,1,2,3,4,...,3995,3996,3997,3998,3999]]
 variant: [[1,0,1,1,0,...,0,0,0,0,0]]
 sessions: [[2,2,2,2,1,...,2,2,3,1,5]]
 orders: [[1,1,1,1,1,...,0,0,0,0,2]]
-revenue: [[9.166147128806545,6.4340787057460656,7.943873223822707,15.928674729738708,7.136917019113867,...,0,0,0,0,17.162458516177704]]
+revenue: [[9.17,6.43,7.94,15.93,7.14,...,0,0,0,0,17.16]]
 has_order: [[1,1,1,1,1,...,0,0,0,0,1]]
 
 ```
@@ -225,7 +225,7 @@ Now we can perform the Mann-Whitney U test:
 >>> print(result.to_string(("metric", "pvalue", "statistic")))
      metric pvalue statistic
  mwu_orders 0.0263   2069092
-mwu_revenue 0.0300   2068063
+mwu_revenue 0.0300   2068060
 
 ```
 
diff --git a/docs/data-backends.md b/docs/data-backends.md
index b1cd740..efcbf31 100644
--- a/docs/data-backends.md
+++ b/docs/data-backends.md
@@ -83,17 +83,17 @@ Ibis Table is a lazy object. It doesn't fetch the data when created. You can use
 ```pycon
 >>> ibis.options.interactive = True
 >>> print(data.head(5))
-┏━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━┓
-┃ user  ┃ variant ┃ sessions ┃ orders ┃ revenue   ┃
-┡━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━┩
-│ int64 │ int64   │ int64    │ int64  │ float64   │
-├───────┼─────────┼──────────┼────────┼───────────┤
-│     0 │       1 │        2 │      1 │  9.166147 │
-│     1 │       0 │        2 │      1 │  6.434079 │
-│     2 │       1 │        2 │      1 │  7.943873 │
-│     3 │       1 │        2 │      1 │ 15.928675 │
-│     4 │       0 │        1 │      1 │  7.136917 │
-└───────┴─────────┴──────────┴────────┴───────────┘
+┏━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓
+┃ user  ┃ variant ┃ sessions ┃ orders ┃ revenue ┃
+┡━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩
+│ int64 │ int64   │ int64    │ int64  │ float64 │
+├───────┼─────────┼──────────┼────────┼─────────┤
+│     0 │       1 │        2 │      1 │    9.17 │
+│     1 │       0 │        2 │      1 │    6.43 │
+│     2 │       1 │        2 │      1 │    7.94 │
+│     3 │       1 │        2 │      1 │   15.93 │
+│     4 │       0 │        1 │      1 │    7.14 │
+└───────┴─────────┴──────────┴────────┴─────────┘
 
 >>> ibis.options.interactive = False
 
@@ -142,8 +142,8 @@ Aggregate[r0]
 ┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩
 │ int64   │ float64           │ float64            │ float64         │ float64          │
 ├─────────┼───────────────────┼────────────────────┼─────────────────┼──────────────────┤
-│       0 │          1.996045 │           0.265726 │        0.530400 │         5.241079 │
-│       1 │          1.982802 │           0.289031 │        0.573091 │         5.730132 │
+│       0 │          1.996045 │           0.265726 │        0.530400 │         5.241028 │
+│       1 │          1.982802 │           0.289031 │        0.573091 │         5.730111 │
 └─────────┴───────────────────┴────────────────────┴─────────────────┴──────────────────┘
 
 >>> ibis.options.interactive = False
@@ -234,7 +234,7 @@ DatabaseTable: memory.main.users_data_with_cov
  sessions_per_user    2.00      1.98          -0.68%      [-3.2%, 1.9%]   0.603
 orders_per_session   0.262     0.293             12%        [4.2%, 21%] 0.00229
    orders_per_user   0.523     0.581             11%        [2.9%, 20%] 0.00733
-  revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00675
+  revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00674
 
 ```
 
diff --git a/docs/multiple-testing.md b/docs/multiple-testing.md
index de99637..91bc6b6 100644
--- a/docs/multiple-testing.md
+++ b/docs/multiple-testing.md
@@ -38,23 +38,23 @@ As an example, consider an experiment with three variants, a control and two tre
 ... ))
 >>> print(data)
 shape: (6_046, 5)
-┌──────┬─────────┬──────────┬────────┬───────────┐
-│ user ┆ variant ┆ sessions ┆ orders ┆ revenue   │
-│ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---       │
-│ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64       │
-╞══════╪═════════╪══════════╪════════╪═══════════╡
-│ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58279   │
-│ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.434079  │
-│ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.304958  │
-│ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.652705 │
-│ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.136917  │
-│ …    ┆ …       ┆ …        ┆ …      ┆ …         │
-│ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.931448 │
-│ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0       │
-│ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.964647 │
-│ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.217892 │
-│ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0       │
-└──────┴─────────┴──────────┴────────┴───────────┘
+┌──────┬─────────┬──────────┬────────┬─────────┐
+│ user ┆ variant ┆ sessions ┆ orders ┆ revenue │
+│ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---     │
+│ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64     │
+╞══════╪═════════╪══════════╪════════╪═════════╡
+│ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58    │
+│ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.43    │
+│ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.3     │
+│ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.65   │
+│ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.14    │
+│ …    ┆ …       ┆ …        ┆ …      ┆ …       │
+│ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.93   │
+│ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0     │
+│ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.96   │
+│ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.22   │
+│ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0     │
+└──────┴─────────┴──────────┴────────┴─────────┘
 
 ```
 
@@ -74,7 +74,7 @@ variants             metric control treatment rel_effect_size rel_effect_size_ci
   (0, 1)  sessions_per_user    2.00      1.98          -0.66%      [-3.7%, 2.5%]   0.674
   (0, 1) orders_per_session   0.266     0.289            8.8%      [-0.89%, 19%]  0.0762
   (0, 1)    orders_per_user   0.530     0.573            8.0%       [-2.0%, 19%]   0.118
-  (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0212
+  (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0211
   (0, 2)  sessions_per_user    2.00      2.02           0.98%      [-2.1%, 4.1%]   0.532
   (0, 2) orders_per_session   0.266     0.295             11%        [1.2%, 22%]  0.0273
   (0, 2)    orders_per_user   0.530     0.594             12%        [1.7%, 23%]  0.0213
@@ -100,7 +100,7 @@ False discovery rate (FDR) is the expected value of the proportion of false disc
 >>> print(adjusted_results_fdr)
 comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
     (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.245
-    (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0592
+    (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0592
     (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0592
     (0, 2) revenue_per_user    5.24      6.25             19% 0.00218     0.0182
 
@@ -122,7 +122,7 @@ The method also adjusts the significance level alpha and saves it as `alpha_adj`
 ... )))
 comparison           metric control treatment rel_effect_size  pvalue alpha_adj
     (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118    0.0240
-    (0, 1) revenue_per_user    5.24      5.99             14%  0.0212    0.0120
+    (0, 1) revenue_per_user    5.24      5.99             14%  0.0211    0.0120
     (0, 2)  orders_per_user   0.530     0.594             12%  0.0213    0.0180
     (0, 2) revenue_per_user    5.24      6.25             19% 0.00218   0.00600
 
@@ -134,9 +134,9 @@ By default, **tea-tasting** assumes arbitrary dependence between hypotheses and
 >>> print(tt.adjust_fdr(results, metrics, arbitrary_dependence=False))
 comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
     (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-    (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0284
+    (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0284
     (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0284
-    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00873
+    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00872
 
 ```
 
@@ -148,9 +148,9 @@ Family-wise error rate (FWER) is the probability of making at least one type I e
 >>> print(tt.adjust_fwer(results, metrics))
 comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
     (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-    (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0635
-    (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0635
-    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00873
+    (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0634
+    (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0634
+    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00872
 
 ```
 
@@ -165,9 +165,9 @@ By default, **tea-tasting** assumes arbitrary dependence between hypotheses and
 ... ))
 comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
     (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-    (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0422
+    (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0422
     (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0422
-    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00870
+    (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00869
 
 ```
 
@@ -188,9 +188,9 @@ In the examples above, the methods `adjust_fdr` and `adjust_fwer` received resul
 ... ))
   comparison           metric control treatment rel_effect_size   pvalue pvalue_adj
 Experiment 1  orders_per_user   0.530     0.573            8.0%    0.118      0.245
-Experiment 1 revenue_per_user    5.24      5.99             14%   0.0212     0.0588
+Experiment 1 revenue_per_user    5.24      5.99             14%   0.0211     0.0587
 Experiment 2  orders_per_user   0.514     0.594             16%  0.00427     0.0178
-Experiment 2 revenue_per_user    5.10      6.25             22% 6.27e-04    0.00523
+Experiment 2 revenue_per_user    5.10      6.25             22% 6.27e-04    0.00522
 
 ```
 
diff --git a/docs/user-guide.md b/docs/user-guide.md
index f66adb4..3505d87 100644
--- a/docs/user-guide.md
+++ b/docs/user-guide.md
@@ -62,7 +62,7 @@ user: [[0,1,2,3,4,...,3995,3996,3997,3998,3999]]
 variant: [[1,0,1,1,0,...,0,0,0,0,0]]
 sessions: [[2,2,2,2,1,...,2,2,3,1,5]]
 orders: [[1,1,1,1,1,...,0,0,0,0,2]]
-revenue: [[9.166147128806545,6.4340787057460656,7.943873223822707,15.928674729738708,7.136917019113867,...,0,0,0,0,17.162458516177704]]
+revenue: [[9.17,6.43,7.94,15.93,7.14,...,0,0,0,0,17.16]]
 
 ```
 
@@ -251,7 +251,7 @@ Example usage:
  sessions_per_user    2.00      1.98          -0.68%      [-3.2%, 1.9%]   0.603
 orders_per_session   0.262     0.293             12%        [4.2%, 21%] 0.00229
    orders_per_user   0.523     0.581             11%        [2.9%, 20%] 0.00733
-  revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00675
+  revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00674
 
 ```
 
diff --git a/src/tea_tasting/datasets.py b/src/tea_tasting/datasets.py
index 5605c98..f6d8874 100644
--- a/src/tea_tasting/datasets.py
+++ b/src/tea_tasting/datasets.py
@@ -153,7 +153,7 @@ def make_users_data(
         variant: [[1,0,1,1,0,...,0,0,0,0,0]]
         sessions: [[2,2,2,2,1,...,2,2,3,1,5]]
         orders: [[1,1,1,1,1,...,0,0,0,0,2]]
-        revenue: [[9.166147128806545,6.4340787057460656,7.943873223822707,15.928674729738708,7.136917019113867,...,0,0,0,0,17.162458516177704]]
+        revenue: [[9.17,6.43,7.94,15.93,7.14,...,0,0,0,0,17.16]]
 
         ```
 
@@ -176,10 +176,10 @@ def make_users_data(
         variant: [[1,0,1,1,0,...,0,0,0,0,0]]
         sessions: [[2,2,2,2,1,...,2,2,3,1,5]]
         orders: [[1,1,1,1,1,...,0,0,0,0,2]]
-        revenue: [[9.166147128806545,6.4340787057460656,7.943873223822707,15.928674729738708,7.136917019113867,...,0,0,0,0,17.162458516177704]]
+        revenue: [[9.17,6.43,7.94,15.93,7.14,...,0,0,0,0,17.16]]
         sessions_covariate: [[3,4,4,1,1,...,1,3,2,1,5]]
         orders_covariate: [[2,1,2,0,1,...,0,1,0,0,0]]
-        revenue_covariate: [[19.191712010123307,2.7707490091913525,22.56842219448677,0,13.683796263730468,...,0,13.517967243105218,0,0,0]]
+        revenue_covariate: [[19.19,2.77,22.57,0,13.68,...,0,13.52,0,0,0]]
 
         ```
 
@@ -188,18 +188,18 @@ def make_users_data(
         ```pycon
         >>> data = tt.make_users_data(seed=42, return_type="pandas")
         >>> print(data)
-              user  variant  sessions  orders    revenue
-        0        0        1         2       1   9.166147
-        1        1        0         2       1   6.434079
-        2        2        1         2       1   7.943873
-        3        3        1         2       1  15.928675
-        4        4        0         1       1   7.136917
-        ...    ...      ...       ...     ...        ...
-        3995  3995        0         2       0   0.000000
-        3996  3996        0         2       0   0.000000
-        3997  3997        0         3       0   0.000000
-        3998  3998        0         1       0   0.000000
-        3999  3999        0         5       2  17.162459
+              user  variant  sessions  orders  revenue
+        0        0        1         2       1     9.17
+        1        1        0         2       1     6.43
+        2        2        1         2       1     7.94
+        3        3        1         2       1    15.93
+        4        4        0         1       1     7.14
+        ...    ...      ...       ...     ...      ...
+        3995  3995        0         2       0     0.00
+        3996  3996        0         2       0     0.00
+        3997  3997        0         3       0     0.00
+        3998  3998        0         1       0     0.00
+        3999  3999        0         5       2    17.16
         <BLANKLINE>
         [4000 rows x 5 columns]
 
@@ -211,26 +211,26 @@ def make_users_data(
         >>> data = tt.make_users_data(seed=42, return_type="polars")
         >>> print(data)
         shape: (4_000, 5)
-        ┌──────┬─────────┬──────────┬────────┬───────────┐
-        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue   │
-        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---       │
-        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64       │
-        ╞══════╪═════════╪══════════╪════════╪═══════════╡
-        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.166147  │
-        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.434079  │
-        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 7.943873  │
-        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 15.928675 │
-        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.136917  │
-        │ …    ┆ …       ┆ …        ┆ …      ┆ …         │
-        │ 3995 ┆ 0       ┆ 2        ┆ 0      ┆ 0.0       │
-        │ 3996 ┆ 0       ┆ 2        ┆ 0      ┆ 0.0       │
-        │ 3997 ┆ 0       ┆ 3        ┆ 0      ┆ 0.0       │
-        │ 3998 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0       │
-        │ 3999 ┆ 0       ┆ 5        ┆ 2      ┆ 17.162459 │
-        └──────┴─────────┴──────────┴────────┴───────────┘
+        ┌──────┬─────────┬──────────┬────────┬─────────┐
+        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue │
+        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---     │
+        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64     │
+        ╞══════╪═════════╪══════════╪════════╪═════════╡
+        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.17    │
+        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.43    │
+        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 7.94    │
+        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 15.93   │
+        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.14    │
+        │ …    ┆ …       ┆ …        ┆ …      ┆ …       │
+        │ 3995 ┆ 0       ┆ 2        ┆ 0      ┆ 0.0     │
+        │ 3996 ┆ 0       ┆ 2        ┆ 0      ┆ 0.0     │
+        │ 3997 ┆ 0       ┆ 3        ┆ 0      ┆ 0.0     │
+        │ 3998 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0     │
+        │ 3999 ┆ 0       ┆ 5        ┆ 2      ┆ 17.16   │
+        └──────┴─────────┴──────────┴────────┴─────────┘
 
         ```
-    """  # noqa: E501
+    """
     return _make_data(
         covariates=covariates,
         seed=seed,
@@ -373,7 +373,7 @@ def make_sessions_data(
         variant: [[1,1,0,0,1,...,0,0,0,0,0]]
         sessions: [[1,1,1,1,1,...,1,1,1,1,1]]
         orders: [[1,1,1,1,1,...,1,0,1,1,0]]
-        revenue: [[5.88717816119309,6.131079903793326,2.614675492093661,12.296074812201192,11.573409274639534,...,23.63494099585371,0,2.396078290493153,24.538111422839766,0]]
+        revenue: [[5.89,6.13,2.61,12.3,11.57,...,23.63,0,2.4,24.54,0]]
 
         ```
 
@@ -396,10 +396,10 @@ def make_sessions_data(
         variant: [[1,1,0,0,1,...,0,0,0,0,0]]
         sessions: [[1,1,1,1,1,...,1,1,1,1,1]]
         orders: [[1,1,1,1,1,...,1,0,1,1,0]]
-        revenue: [[5.88717816119309,6.131079903793326,2.614675492093661,12.296074812201192,11.573409274639534,...,23.63494099585371,0,2.396078290493153,24.538111422839766,0]]
+        revenue: [[5.89,6.13,2.61,12.3,11.57,...,23.63,0,2.4,24.54,0]]
         sessions_covariate: [[1.5,1.5,0,0,1.5,...,0.2,0.2,0.2,0.2,0.2]]
         orders_covariate: [[0.5,0.5,0,0,1.5,...,0,0,0,0,0]]
-        revenue_covariate: [[1.2367323749905585,1.2367323749905585,0,0,12.324434081065741,...,0,0,0,0,0]]
+        revenue_covariate: [[1.24,1.24,0,0,12.32,...,0,0,0,0,0]]
 
         ```
 
@@ -408,18 +408,18 @@ def make_sessions_data(
         ```pycon
         >>> data = tt.make_sessions_data(seed=42, return_type="pandas")
         >>> print(data)
-              user  variant  sessions  orders    revenue
-        0        0        1         1       1   5.887178
-        1        0        1         1       1   6.131080
-        2        1        0         1       1   2.614675
-        3        1        0         1       1  12.296075
-        4        2        1         1       1  11.573409
-        ...    ...      ...       ...     ...        ...
-        7953  3999        0         1       1  23.634941
-        7954  3999        0         1       0   0.000000
-        7955  3999        0         1       1   2.396078
-        7956  3999        0         1       1  24.538111
-        7957  3999        0         1       0   0.000000
+              user  variant  sessions  orders  revenue
+        0        0        1         1       1     5.89
+        1        0        1         1       1     6.13
+        2        1        0         1       1     2.61
+        3        1        0         1       1    12.30
+        4        2        1         1       1    11.57
+        ...    ...      ...       ...     ...      ...
+        7953  3999        0         1       1    23.63
+        7954  3999        0         1       0     0.00
+        7955  3999        0         1       1     2.40
+        7956  3999        0         1       1    24.54
+        7957  3999        0         1       0     0.00
         <BLANKLINE>
         [7958 rows x 5 columns]
 
@@ -431,26 +431,26 @@ def make_sessions_data(
         >>> data = tt.make_sessions_data(seed=42, return_type="polars")
         >>> print(data)
         shape: (7_958, 5)
-        ┌──────┬─────────┬──────────┬────────┬───────────┐
-        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue   │
-        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---       │
-        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64       │
-        ╞══════╪═════════╪══════════╪════════╪═══════════╡
-        │ 0    ┆ 1       ┆ 1        ┆ 1      ┆ 5.887178  │
-        │ 0    ┆ 1       ┆ 1        ┆ 1      ┆ 6.13108   │
-        │ 1    ┆ 0       ┆ 1        ┆ 1      ┆ 2.614675  │
-        │ 1    ┆ 0       ┆ 1        ┆ 1      ┆ 12.296075 │
-        │ 2    ┆ 1       ┆ 1        ┆ 1      ┆ 11.573409 │
-        │ …    ┆ …       ┆ …        ┆ …      ┆ …         │
-        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 23.634941 │
-        │ 3999 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0       │
-        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 2.396078  │
-        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 24.538111 │
-        │ 3999 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0       │
-        └──────┴─────────┴──────────┴────────┴───────────┘
+        ┌──────┬─────────┬──────────┬────────┬─────────┐
+        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue │
+        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---     │
+        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64     │
+        ╞══════╪═════════╪══════════╪════════╪═════════╡
+        │ 0    ┆ 1       ┆ 1        ┆ 1      ┆ 5.89    │
+        │ 0    ┆ 1       ┆ 1        ┆ 1      ┆ 6.13    │
+        │ 1    ┆ 0       ┆ 1        ┆ 1      ┆ 2.61    │
+        │ 1    ┆ 0       ┆ 1        ┆ 1      ┆ 12.3    │
+        │ 2    ┆ 1       ┆ 1        ┆ 1      ┆ 11.57   │
+        │ …    ┆ …       ┆ …        ┆ …      ┆ …       │
+        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 23.63   │
+        │ 3999 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0     │
+        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 2.4     │
+        │ 3999 ┆ 0       ┆ 1        ┆ 1      ┆ 24.54   │
+        │ 3999 ┆ 0       ┆ 1        ┆ 0      ┆ 0.0     │
+        └──────┴─────────┴──────────┴────────┴─────────┘
 
         ```
-    """  # noqa: E501
+    """
     return _make_data(
         covariates=covariates,
         seed=seed,
@@ -537,7 +537,7 @@ def _make_data(
         "variant": variant[user],
         "sessions": sessions,
         "orders": orders,
-        "revenue": revenue,
+        "revenue": revenue.round(2),
     }
 
     if covariates:
@@ -569,7 +569,7 @@ def _make_data(
         data |= {
             "sessions_covariate": sessions_covariate,
             "orders_covariate": orders_covariate,
-            "revenue_covariate": revenue_covariate,
+            "revenue_covariate": revenue_covariate.round(2),
         }
 
     if return_type == "pandas":
diff --git a/src/tea_tasting/experiment.py b/src/tea_tasting/experiment.py
index 649fac6..3640d8b 100644
--- a/src/tea_tasting/experiment.py
+++ b/src/tea_tasting/experiment.py
@@ -63,17 +63,17 @@ def to_dicts(self) -> tuple[dict[str, Any], ...]:
               'rel_effect_size_ci_upper': 0.1906880061278886,
               'statistic': 1.5647028839586707,
               'treatment': 0.5730905412240769},
-             {'control': 5.241078645860599,
-              'effect_size': 0.48905301100469245,
-              'effect_size_ci_lower': -0.13265634499246626,
-              'effect_size_ci_upper': 1.1107623670018512,
+             {'control': 5.241028175976273,
+              'effect_size': 0.4890831037404775,
+              'effect_size_ci_lower': -0.13261881482742033,
+              'effect_size_ci_upper': 1.1107850223083753,
               'metric': 'revenue_per_user',
-              'pvalue': np.float64(0.1230974173674023),
-              'rel_effect_size': 0.09331151925967496,
-              'rel_effect_size_ci_lower': -0.023744208691728885,
-              'rel_effect_size_ci_upper': 0.22440254776265967,
-              'statistic': 1.5422307220453753,
-              'treatment': 5.730131656865291})
+              'pvalue': np.float64(0.1230698855425058),
+              'rel_effect_size': 0.09331815958981626,
+              'rel_effect_size_ci_lower': -0.02373770894855798,
+              'rel_effect_size_ci_upper': 0.22440926894909308,
+              'statistic': 1.5423440700784083,
+              'treatment': 5.73011127971675})
 
             ```
         """
diff --git a/src/tea_tasting/metrics/mean.py b/src/tea_tasting/metrics/mean.py
index e0b3c1c..4368e3b 100644
--- a/src/tea_tasting/metrics/mean.py
+++ b/src/tea_tasting/metrics/mean.py
@@ -823,7 +823,7 @@ def __init__(  # noqa: PLR0913
             >>> print(result)
                       metric control treatment rel_effect_size rel_effect_size_ci  pvalue
              orders_per_user   0.523     0.581             11%        [2.9%, 20%] 0.00733
-            revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00675
+            revenue_per_user    5.12      5.85             14%        [3.8%, 26%] 0.00674
 
             ```
 
diff --git a/src/tea_tasting/metrics/resampling.py b/src/tea_tasting/metrics/resampling.py
index 39d1125..833d9f9 100644
--- a/src/tea_tasting/metrics/resampling.py
+++ b/src/tea_tasting/metrics/resampling.py
@@ -333,7 +333,7 @@ def __init__(
             >>> result = experiment.analyze(data)
             >>> print(result)
                           metric control treatment rel_effect_size rel_effect_size_ci pvalue
-            revenue_per_user_p80    10.6      11.6            9.1%       [-1.3%, 21%]      -
+            revenue_per_user_p80    10.6      11.6            9.1%       [-1.2%, 21%]      -
 
             ```
         """  # noqa: E501
diff --git a/src/tea_tasting/multiplicity.py b/src/tea_tasting/multiplicity.py
index 434e63b..06322cd 100644
--- a/src/tea_tasting/multiplicity.py
+++ b/src/tea_tasting/multiplicity.py
@@ -119,23 +119,23 @@ def adjust_fdr(
         ... ))
         >>> print(data)
         shape: (6_046, 5)
-        ┌──────┬─────────┬──────────┬────────┬───────────┐
-        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue   │
-        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---       │
-        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64       │
-        ╞══════╪═════════╪══════════╪════════╪═══════════╡
-        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58279   │
-        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.434079  │
-        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.304958  │
-        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.652705 │
-        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.136917  │
-        │ …    ┆ …       ┆ …        ┆ …      ┆ …         │
-        │ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.931448 │
-        │ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0       │
-        │ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.964647 │
-        │ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.217892 │
-        │ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0       │
-        └──────┴─────────┴──────────┴────────┴───────────┘
+        ┌──────┬─────────┬──────────┬────────┬─────────┐
+        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue │
+        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---     │
+        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64     │
+        ╞══════╪═════════╪══════════╪════════╪═════════╡
+        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58    │
+        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.43    │
+        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.3     │
+        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.65   │
+        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.14    │
+        │ …    ┆ …       ┆ …        ┆ …      ┆ …       │
+        │ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.93   │
+        │ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0     │
+        │ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.96   │
+        │ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.22   │
+        │ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0     │
+        └──────┴─────────┴──────────┴────────┴─────────┘
 
         >>> experiment = tt.Experiment(
         ...     sessions_per_user=tt.Mean("sessions"),
@@ -151,7 +151,7 @@ def adjust_fdr(
           (0, 1)  sessions_per_user    2.00      1.98          -0.66%      [-3.7%, 2.5%]   0.674
           (0, 1) orders_per_session   0.266     0.289            8.8%      [-0.89%, 19%]  0.0762
           (0, 1)    orders_per_user   0.530     0.573            8.0%       [-2.0%, 19%]   0.118
-          (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0212
+          (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0211
           (0, 2)  sessions_per_user    2.00      2.02           0.98%      [-2.1%, 4.1%]   0.532
           (0, 2) orders_per_session   0.266     0.295             11%        [1.2%, 22%]  0.0273
           (0, 2)    orders_per_user   0.530     0.594             12%        [1.7%, 23%]  0.0213
@@ -166,7 +166,7 @@ def adjust_fdr(
         >>> print(adjusted_results_fdr)
         comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.245
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0592
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0592
             (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0592
             (0, 2) revenue_per_user    5.24      6.25             19% 0.00218     0.0182
 
@@ -182,7 +182,7 @@ def adjust_fdr(
         ... )))
         comparison           metric control treatment rel_effect_size  pvalue alpha_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118    0.0240
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212    0.0120
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211    0.0120
             (0, 2)  orders_per_user   0.530     0.594             12%  0.0213    0.0180
             (0, 2) revenue_per_user    5.24      6.25             19% 0.00218   0.00600
 
@@ -191,9 +191,9 @@ def adjust_fdr(
         >>> print(tt.adjust_fdr(results, metrics, arbitrary_dependence=False))
         comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0284
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0284
             (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0284
-            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00873
+            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00872
 
         ```
     """  # noqa: E501
@@ -297,23 +297,23 @@ def adjust_fwer(
         ... ))
         >>> print(data)
         shape: (6_046, 5)
-        ┌──────┬─────────┬──────────┬────────┬───────────┐
-        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue   │
-        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---       │
-        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64       │
-        ╞══════╪═════════╪══════════╪════════╪═══════════╡
-        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58279   │
-        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.434079  │
-        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.304958  │
-        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.652705 │
-        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.136917  │
-        │ …    ┆ …       ┆ …        ┆ …      ┆ …         │
-        │ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.931448 │
-        │ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0       │
-        │ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.964647 │
-        │ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.217892 │
-        │ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0       │
-        └──────┴─────────┴──────────┴────────┴───────────┘
+        ┌──────┬─────────┬──────────┬────────┬─────────┐
+        │ user ┆ variant ┆ sessions ┆ orders ┆ revenue │
+        │ ---  ┆ ---     ┆ ---      ┆ ---    ┆ ---     │
+        │ i64  ┆ i64     ┆ i64      ┆ i64    ┆ f64     │
+        ╞══════╪═════════╪══════════╪════════╪═════════╡
+        │ 0    ┆ 1       ┆ 2        ┆ 1      ┆ 9.58    │
+        │ 1    ┆ 0       ┆ 2        ┆ 1      ┆ 6.43    │
+        │ 2    ┆ 1       ┆ 2        ┆ 1      ┆ 8.3     │
+        │ 3    ┆ 1       ┆ 2        ┆ 1      ┆ 16.65   │
+        │ 4    ┆ 0       ┆ 1        ┆ 1      ┆ 7.14    │
+        │ …    ┆ …       ┆ …        ┆ …      ┆ …       │
+        │ 3989 ┆ 2       ┆ 4        ┆ 4      ┆ 34.93   │
+        │ 3991 ┆ 2       ┆ 1        ┆ 0      ┆ 0.0     │
+        │ 3992 ┆ 2       ┆ 3        ┆ 3      ┆ 27.96   │
+        │ 3994 ┆ 2       ┆ 2        ┆ 1      ┆ 17.22   │
+        │ 3998 ┆ 2       ┆ 3        ┆ 0      ┆ 0.0     │
+        └──────┴─────────┴──────────┴────────┴─────────┘
 
         >>> experiment = tt.Experiment(
         ...     sessions_per_user=tt.Mean("sessions"),
@@ -329,7 +329,7 @@ def adjust_fwer(
           (0, 1)  sessions_per_user    2.00      1.98          -0.66%      [-3.7%, 2.5%]   0.674
           (0, 1) orders_per_session   0.266     0.289            8.8%      [-0.89%, 19%]  0.0762
           (0, 1)    orders_per_user   0.530     0.573            8.0%       [-2.0%, 19%]   0.118
-          (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0212
+          (0, 1)   revenue_per_user    5.24      5.99             14%        [2.1%, 28%]  0.0211
           (0, 2)  sessions_per_user    2.00      2.02           0.98%      [-2.1%, 4.1%]   0.532
           (0, 2) orders_per_session   0.266     0.295             11%        [1.2%, 22%]  0.0273
           (0, 2)    orders_per_user   0.530     0.594             12%        [1.7%, 23%]  0.0213
@@ -344,9 +344,9 @@ def adjust_fwer(
         >>> print(adjusted_results_fwer)
         comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0635
-            (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0635
-            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00873
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0634
+            (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0634
+            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00872
 
         >>> # The adjusted confidence level alpha.
         >>> print(adjusted_results_fwer.to_string(keys=(
@@ -360,7 +360,7 @@ def adjust_fwer(
         ... )))
         comparison           metric control treatment rel_effect_size  pvalue alpha_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118    0.0167
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212    0.0167
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211    0.0167
             (0, 2)  orders_per_user   0.530     0.594             12%  0.0213    0.0167
             (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.0125
 
@@ -374,9 +374,9 @@ def adjust_fwer(
         ... ))
         comparison           metric control treatment rel_effect_size  pvalue pvalue_adj
             (0, 1)  orders_per_user   0.530     0.573            8.0%   0.118      0.118
-            (0, 1) revenue_per_user    5.24      5.99             14%  0.0212     0.0422
+            (0, 1) revenue_per_user    5.24      5.99             14%  0.0211     0.0422
             (0, 2)  orders_per_user   0.530     0.594             12%  0.0213     0.0422
-            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00870
+            (0, 2) revenue_per_user    5.24      6.25             19% 0.00218    0.00869
 
         ```
     """  # noqa: E501, RUF002
diff --git a/tests/metrics/test_resampling.py b/tests/metrics/test_resampling.py
index 01ed997..b175c92 100644
--- a/tests/metrics/test_resampling.py
+++ b/tests/metrics/test_resampling.py
@@ -87,14 +87,14 @@ def test_bootstrap_analyze_default(data_gran: dict[Any, pa.Table]):
     )
     result = metric.analyze(data_gran, 0, 1)
     assert isinstance(result, tea_tasting.metrics.resampling.BootstrapResult)
-    assert result.control == pytest.approx(5.029606016096378)
-    assert result.treatment == pytest.approx(5.430045947447926)
-    assert result.effect_size == pytest.approx(0.4004399313515483)
-    assert result.effect_size_ci_lower == pytest.approx(-3.269115518352006)
-    assert result.effect_size_ci_upper == pytest.approx(7.220410053935425)
-    assert result.rel_effect_size == pytest.approx(0.07961656043634635)
-    assert result.rel_effect_size_ci_lower == pytest.approx(-0.5658060166766641)
-    assert result.rel_effect_size_ci_upper == pytest.approx(1.8185107973505807)
+    assert result.control == pytest.approx(5.029811320754717)
+    assert result.treatment == pytest.approx(5.43)
+    assert result.effect_size == pytest.approx(0.4001886792452831)
+    assert result.effect_size_ci_lower == pytest.approx(-3.269396309565539)
+    assert result.effect_size_ci_upper == pytest.approx(7.219843380442667)
+    assert result.rel_effect_size == pytest.approx(0.07956335809137971)
+    assert result.rel_effect_size_ci_lower == pytest.approx(-0.5658493834599828)
+    assert result.rel_effect_size_ci_upper == pytest.approx(1.8185473860534842)
 
 def test_bootstrap_analyze_multiple_columns(data_gran: dict[Any, pa.Table]):
     def ratio_of_means(
@@ -153,11 +153,11 @@ def test_quantile(data_gran: dict[Any, pa.Table]):
     assert metric.q == 0.8
     result = metric.analyze(data_gran, 0, 1)
     assert isinstance(result, tea_tasting.metrics.resampling.BootstrapResult)
-    assert result.control == pytest.approx(11.97241622964322)
-    assert result.treatment == pytest.approx(6.283899054876212)
-    assert result.effect_size == pytest.approx(-5.688517174767009)
-    assert result.effect_size_ci_lower == pytest.approx(-10.875502551863555)
+    assert result.control == pytest.approx(11.972000000000001)
+    assert result.treatment == pytest.approx(6.2820000000000045)
+    assert result.effect_size == pytest.approx(-5.689999999999997)
+    assert result.effect_size_ci_lower == pytest.approx(-10.875800000000003)
     assert result.effect_size_ci_upper == float("inf")
-    assert result.rel_effect_size == pytest.approx(-0.4751352664036579 )
-    assert result.rel_effect_size_ci_lower == pytest.approx(-0.8744367099313992)
+    assert result.rel_effect_size == pytest.approx(-0.47527564316739024)
+    assert result.rel_effect_size_ci_lower == pytest.approx(-0.8743329817472134)
     assert result.rel_effect_size_ci_upper == float("inf")