diff --git a/pytorch_lightning/trainer/data_loading.py b/pytorch_lightning/trainer/data_loading.py
index e036e7e90254d..4460e235b11bd 100644
--- a/pytorch_lightning/trainer/data_loading.py
+++ b/pytorch_lightning/trainer/data_loading.py
@@ -171,7 +171,7 @@ def _resolve_sampler(self, dataloader: DataLoader, shuffle: bool, mode: Optional
         if self._requires_distributed_sampler(dataloader):
             if not isinstance(dataloader.sampler, (SequentialSampler, RandomSampler)):
                 raise MisconfigurationException(
-                    "You seem to have configured a sampler in your DataLoader. This will be replaced "
+                    "You seem to have configured a sampler in your DataLoader. This will be replaced"
                     " by `DistributedSampler` since `replace_sampler_ddp` is True and you are using"
                     " distributed training. Either remove the sampler from your DataLoader or set"
                     " `replace_sampler_ddp=False` if you want to use your custom sampler."
@@ -322,7 +322,7 @@ def _reset_eval_dataloader(
         module = model or self.lightning_module or self.datamodule
         if len(dataloaders) != 0:
             for i, dataloader in enumerate(dataloaders):
-                num_batches = (
+                orig_num_batches = num_batches = (
                     len(dataloader)
                     if has_len_all_ranks(dataloader, self.training_type_plugin, module)
                     else float("inf")
@@ -348,7 +348,7 @@ def _reset_eval_dataloader(
                     min_pct = 1.0 / len(dataloader)
                     raise MisconfigurationException(
                         f"you requested to check {limit_eval_batches} of the `{mode.dataloader_prefix}_dataloader` but"
-                        f" {limit_eval_batches}*{num_batches} < 1. Please increase the"
+                        f" {limit_eval_batches} * {orig_num_batches} < 1. Please increase the"
                         f" `limit_{mode.dataloader_prefix}_batches` flag. Try at least"
                         f" `limit_{mode.dataloader_prefix}_batches={min_pct}`"
                     )
diff --git a/tests/trainer/test_data_loading.py b/tests/trainer/test_data_loading.py
index 74c074aef2110..95b9f9061b366 100644
--- a/tests/trainer/test_data_loading.py
+++ b/tests/trainer/test_data_loading.py
@@ -268,7 +268,7 @@ class CustomSampler(Sampler):
 
     # Should raise an error if existing sampler is being replaced
     dataloader = CustomDataLoader(dataset, sampler=CustomSampler(dataset))
-    with pytest.raises(MisconfigurationException, match="will be replaced  by `DistributedSampler`"):
+    with pytest.raises(MisconfigurationException, match="will be replaced by `DistributedSampler`"):
         trainer.prepare_dataloader(dataloader, shuffle=True)
 
 
@@ -339,6 +339,21 @@ def test_pre_made_batches():
     trainer.predict(LoaderTestModel(), loader)
 
 
+def test_error_raised_with_float_limited_eval_batches():
+    """Test that an error is raised if there are not enough batches when passed with float value of
+    limit_eval_batches."""
+    model = BoringModel()
+    dl_size = len(model.val_dataloader())
+    limit_val_batches = 1 / (dl_size + 2)
+    trainer = Trainer(limit_val_batches=limit_val_batches)
+    trainer._data_connector.attach_data(model)
+    with pytest.raises(
+        MisconfigurationException,
+        match=fr"{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`",
+    ):
+        trainer._reset_eval_dataloader(RunningStage.VALIDATING, model)
+
+
 @pytest.mark.parametrize(
     "val_dl",
     [