diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py
index 26f7762ca9b5..0a4d17dc2668 100644
--- a/python/mxnet/test_utils.py
+++ b/python/mxnet/test_utils.py
@@ -1849,12 +1849,12 @@ def chi_square_check(generator, buckets, probs, nsamples=1000000):
 
     If the generator is continuous, the buckets should contain tuples of (range_min, range_max) \
     and the probs should be the corresponding ideal probability within the specific ranges. \
-    Otherwise, the buckets should be the possible output of the discrete distribution and the \
+    Otherwise, the buckets should contain all the possible values generated over the discrete distribution and the \
     probs should be groud-truth probability.
 
     Usually the user is required to specify the probs parameter.
 
-    After obtatining the p value, we could further use the standard p > 0.05 threshold to get \
+    After obtaining the p value, we could further use the standard p > 0.05 (alpha) threshold to get \
     the final result.
 
     Examples::
@@ -1906,7 +1906,6 @@ def chi_square_check(generator, buckets, probs, nsamples=1000000):
             buckets_npy[i * 2 + 1] = buckets[i][1]
     else:
         continuous_dist = False
-        buckets_npy = np.array(buckets)
     expected_freq = (nsamples * np.array(probs, dtype=np.float32)).astype(np.int32)
     if continuous_dist:
         sample_bucket_ids = np.searchsorted(buckets_npy, samples, side='right')
@@ -1923,7 +1922,7 @@ def chi_square_check(generator, buckets, probs, nsamples=1000000):
     _, p = ss.chisquare(f_obs=obs_freq, f_exp=expected_freq)
     return p, obs_freq, expected_freq
 
-def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, success_rate=0.15):
+def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, success_rate=0.25, alpha=0.05):
     """Verify whether the generator is correct using chi-square testing.
 
     The test is repeated for "nrepeat" times and we check if the success rate is
@@ -1946,6 +1945,8 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
         The times to repeat the test
     success_rate: float
         The desired success rate
+    alpha: float
+        The desired threshold for type-I error i.e. when a true null hypothesis is rejected
 
     Returns
     -------
@@ -1961,7 +1962,7 @@ def verify_generator(generator, buckets, probs, nsamples=1000000, nrepeat=5, suc
         cs_ret_l.append(cs_ret)
         obs_freq_l.append(obs_freq)
         expected_freq_l.append(expected_freq)
-    success_num = (np.array(cs_ret_l) > 0.05).sum()
+    success_num = (np.array(cs_ret_l) > alpha).sum()
     if success_num < nrepeat * success_rate:
         raise AssertionError("Generator test fails, Chi-square p=%s, obs_freq=%s, expected_freq=%s."
                              "\nbuckets=%s, probs=%s"
diff --git a/tests/python/unittest/test_random.py b/tests/python/unittest/test_random.py
index 3026d31c0f96..405602f073bb 100644
--- a/tests/python/unittest/test_random.py
+++ b/tests/python/unittest/test_random.py
@@ -860,25 +860,26 @@ def test_randint_extremes():
     assert a>=50000000 and a<=50000010
 
 @with_seed()
-@unittest.skip("Flaky test: https://github.com/apache/incubator-mxnet/issues/13446")
 def test_randint_generator():
     ctx = mx.context.current_context()
     for dtype in ['int32', 'int64']:
-        for low, high in [(50000000, 50001000),(-50000000,-9900),(-500,199),(-2147483647,2147483647)]:
+        for low, high in [(50000000, 50001000),(-50000100,-50000000),(-500,199)]:
             scale = high - low
             buckets, probs = gen_buckets_probs_with_ppf(lambda x: ss.uniform.ppf(x, loc=low, scale=scale), 5)
             # Quantize bucket boundaries to reflect the actual dtype and adjust probs accordingly
             buckets = np.array(buckets, dtype=dtype).tolist()
             probs = [(buckets[i][1] - buckets[i][0]) / float(scale) for i in range(5)]
             generator_mx = lambda x: mx.nd.random.randint(low, high, shape=x, ctx=ctx, dtype=dtype).asnumpy()
-            verify_generator(generator=generator_mx, buckets=buckets, probs=probs)
+            verify_generator(generator=generator_mx, buckets=buckets, probs=probs, nrepeat=100)
+            # Scipy uses alpha = 0.01 for testing discrete distribution generator but we are using default alpha=0.05 (higher threshold ensures robustness)
+            # Refer - https://github.com/scipy/scipy/blob/9f12af697763fb5f9767d5cb1280ce62456a3974/scipy/stats/tests/test_discrete_basic.py#L45
             generator_mx_same_seed = \
                 lambda x: np.concatenate(
                     [mx.nd.random.randint(low, high, shape=x // 10, ctx=ctx, dtype=dtype).asnumpy()
                         for _ in range(10)])
-            verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs)
+            verify_generator(generator=generator_mx_same_seed, buckets=buckets, probs=probs, nrepeat=100)
 
-with_seed()
+@with_seed()
 def test_randint_without_dtype():
     a = mx.nd.random.randint(low=50000000, high=50000010, ctx=mx.context.current_context())
     assert(a.dtype, 'int32')