diff --git a/ivy/functional/backends/numpy/experimental/linear_algebra.py b/ivy/functional/backends/numpy/experimental/linear_algebra.py
index 915b0b4ad9111..c46ad3b7dde4d 100644
--- a/ivy/functional/backends/numpy/experimental/linear_algebra.py
+++ b/ivy/functional/backends/numpy/experimental/linear_algebra.py
@@ -3,7 +3,7 @@
 import numpy as np
 
 import ivy
-from ivy.func_wrapper import with_supported_dtypes
+from ivy.func_wrapper import with_supported_dtypes, with_unsupported_dtypes
 from ivy.utils.exceptions import IvyNotImplementedException
 from .. import backend_version
 
@@ -114,14 +114,13 @@ def matrix_exp(
     return exp_mat.astype(x.dtype)
 
 
+@with_unsupported_dtypes({"1.25.2 and below": ("float16",)}, backend_version)
 def eig(
     x: np.ndarray,
     /,
     *,
     out: Optional[np.ndarray] = None,
-) -> Tuple[np.ndarray]:
-    if ivy.dtype(x) == ivy.float16:
-        x = x.astype(np.float32)
+) -> Tuple[np.ndarray, np.ndarray]:
     e, v = np.linalg.eig(x)
     return e.astype(complex), v.astype(complex)
 
@@ -129,9 +128,8 @@ def eig(
 eig.support_native_out = False
 
 
+@with_unsupported_dtypes({"1.25.2 and below": ("float16",)}, backend_version)
 def eigvals(x: np.ndarray, /) -> np.ndarray:
-    if ivy.dtype(x) == ivy.float16:
-        x = x.astype(np.float32)
     e = np.linalg.eigvals(x)
     return e.astype(complex)
 
diff --git a/ivy/functional/backends/numpy/experimental/statistical.py b/ivy/functional/backends/numpy/experimental/statistical.py
index 667c6106c61a3..a6638f8bdea49 100644
--- a/ivy/functional/backends/numpy/experimental/statistical.py
+++ b/ivy/functional/backends/numpy/experimental/statistical.py
@@ -428,13 +428,6 @@ def cummax(
     dtype: Optional[np.dtype] = None,
     out: Optional[np.ndarray] = None,
 ) -> Tuple[np.ndarray, np.ndarray]:
-    if x.dtype in (np.bool_, np.float16):
-        x = x.astype(np.float64)
-    elif x.dtype in (np.int16, np.int8, np.uint8):
-        x = x.astype(np.int64)
-    elif x.dtype in (np.complex128, np.complex64):
-        x = np.real(x).astype(np.float64)
-
     if exclusive or reverse:
         if exclusive and reverse:
             indices = __find_cummax_indices(np.flip(x, axis=axis), axis=axis)
@@ -527,10 +520,7 @@ def cummin(
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if dtype is None:
-        if x.dtype == "bool":
-            dtype = ivy.default_int_dtype(as_native=True)
-        else:
-            dtype = _infer_dtype(x.dtype)
+        dtype = _infer_dtype(x.dtype)
     if not (reverse):
         return np.minimum.accumulate(x, axis, dtype=dtype, out=out)
     elif reverse:
diff --git a/ivy/functional/backends/numpy/statistical.py b/ivy/functional/backends/numpy/statistical.py
index 419ef9355d336..c5ef5b472901d 100644
--- a/ivy/functional/backends/numpy/statistical.py
+++ b/ivy/functional/backends/numpy/statistical.py
@@ -171,7 +171,7 @@ def var(
 # ------#
 
 
-@with_unsupported_dtypes({"1.25.2 and below": "bfloat16"}, backend_version)
+@with_unsupported_dtypes({"1.25.2 and below": ("bfloat16",)}, backend_version)
 def cumprod(
     x: np.ndarray,
     /,
@@ -183,10 +183,7 @@ def cumprod(
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if dtype is None:
-        if x.dtype == "bool":
-            dtype = ivy.default_int_dtype(as_native=True)
-        else:
-            dtype = _infer_dtype(x.dtype)
+        dtype = _infer_dtype(x.dtype)
     if not (exclusive or reverse):
         return np.cumprod(x, axis, dtype=dtype, out=out)
     elif exclusive and reverse:
@@ -218,10 +215,6 @@ def cumsum(
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
     if dtype is None:
-        if x.dtype == "bool":
-            dtype = ivy.default_int_dtype(as_native=True)
-        if ivy.is_int_dtype(x.dtype):
-            dtype = ivy.promote_types(x.dtype, ivy.default_int_dtype(as_native=True))
         dtype = _infer_dtype(x.dtype)
 
     if exclusive or reverse:
diff --git a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
index 43e74bf3c51ca..e584d4136af7f 100644
--- a/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
+++ b/ivy_tests/test_ivy/test_functional/test_core/test_statistical.py
@@ -14,7 +14,7 @@
 
 @st.composite
 def _get_castable_dtype(draw, min_value=None, max_value=None):
-    available_dtypes = helpers.get_dtypes("numeric")
+    available_dtypes = helpers.get_dtypes("valid")
     shape = draw(helpers.get_shape(min_num_dims=1, max_num_dims=4, max_dim_size=6))
     dtype, values = draw(
         helpers.dtype_and_values(
diff --git a/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_linalg.py b/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_linalg.py
index b1b01c92b0a53..3b367f854b3b2 100644
--- a/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_linalg.py
+++ b/ivy_tests/test_ivy/test_functional/test_experimental/test_core/test_linalg.py
@@ -895,12 +895,13 @@ def test_dot(*, data, test_flags, backend_fw, fn_name, on_device):
     test_with_out=st.just(False),
     test_gradients=st.just(False),
 )
-def test_eig(dtype_x, test_flags, backend_fw, fn_name):
+def test_eig(dtype_x, test_flags, backend_fw, fn_name, on_device):
     dtype, x = dtype_x
     helpers.test_function(
         input_dtypes=dtype,
         test_flags=test_flags,
         backend_to_test=backend_fw,
+        on_device=on_device,
         fn_name=fn_name,
         test_values=False,
         x=x[0],
@@ -1002,12 +1003,13 @@ def test_eigh_tridiagonal(
     test_with_out=st.just(False),
     test_gradients=st.just(False),
 )
-def test_eigvals(dtype_x, test_flags, backend_fw, fn_name):
+def test_eigvals(dtype_x, test_flags, backend_fw, fn_name, on_device):
     dtype, x = dtype_x
     helpers.test_function(
         input_dtypes=dtype,
         test_flags=test_flags,
         backend_to_test=backend_fw,
+        on_device=on_device,
         fn_name=fn_name,
         test_values=False,
         x=x[0],