diff --git a/pandas/conftest.py b/pandas/conftest.py
index b6e1559b9f8cf..cf735bf535ddd 100644
--- a/pandas/conftest.py
+++ b/pandas/conftest.py
@@ -1603,6 +1603,45 @@ def any_numpy_dtype(request):
     return request.param
 
 
+@pytest.fixture(
+    params=tm.ALL_REAL_NUMPY_DTYPES
+    + tm.COMPLEX_DTYPES
+    + tm.ALL_INT_EA_DTYPES
+    + tm.FLOAT_EA_DTYPES
+)
+def any_numeric_dtype(request):
+    """
+    Parameterized fixture for all numeric dtypes.
+
+    * int
+    * 'int8'
+    * 'uint8'
+    * 'int16'
+    * 'uint16'
+    * 'int32'
+    * 'uint32'
+    * 'int64'
+    * 'uint64'
+    * float
+    * 'float32'
+    * 'float64'
+    * complex
+    * 'complex64'
+    * 'complex128'
+    * 'UInt8'
+    * 'Int8'
+    * 'UInt16'
+    * 'Int16'
+    * 'UInt32'
+    * 'Int32'
+    * 'UInt64'
+    * 'Int64'
+    * 'Float32'
+    * 'Float64'
+    """
+    return request.param
+
+
 # categoricals are handled separately
 _any_skipna_inferred_dtype = [
     ("string", ["a", np.nan, "c"]),
diff --git a/pandas/core/describe.py b/pandas/core/describe.py
index d265a307078b9..d6546b06ec711 100644
--- a/pandas/core/describe.py
+++ b/pandas/core/describe.py
@@ -32,6 +32,7 @@
 
 from pandas.core.dtypes.common import (
     is_bool_dtype,
+    is_complex_dtype,
     is_datetime64_any_dtype,
     is_numeric_dtype,
     is_timedelta64_dtype,
@@ -240,7 +241,9 @@ def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series:
         + series.quantile(percentiles).tolist()
         + [series.max()]
     )
-    return Series(d, index=stat_index, name=series.name)
+    # GH#48340 - always return float on non-complex numeric data
+    dtype = float if is_numeric_dtype(series) and not is_complex_dtype(series) else None
+    return Series(d, index=stat_index, name=series.name, dtype=dtype)
 
 
 def describe_categorical_1d(
diff --git a/pandas/tests/series/methods/test_describe.py b/pandas/tests/series/methods/test_describe.py
index e6c6016d2b3a1..d7650e2768781 100644
--- a/pandas/tests/series/methods/test_describe.py
+++ b/pandas/tests/series/methods/test_describe.py
@@ -1,5 +1,7 @@
 import numpy as np
 
+from pandas.core.dtypes.common import is_complex_dtype
+
 from pandas import (
     Period,
     Series,
@@ -149,3 +151,23 @@ def test_datetime_is_numeric_includes_datetime(self):
             index=["count", "mean", "min", "25%", "50%", "75%", "max"],
         )
         tm.assert_series_equal(result, expected)
+
+    def test_numeric_result_dtype(self, any_numeric_dtype):
+        # GH#48340 - describe should always return float on non-complex numeric input
+        ser = Series([0, 1], dtype=any_numeric_dtype)
+        result = ser.describe()
+        expected = Series(
+            [
+                2.0,
+                0.5,
+                ser.std(),
+                0,
+                0.25,
+                0.5,
+                0.75,
+                1.0,
+            ],
+            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+            dtype="complex128" if is_complex_dtype(ser) else None,
+        )
+        tm.assert_series_equal(result, expected)