diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 5e8407f79a..233638361a 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -620,14 +620,14 @@ class LightningModule(
             for val_batch in val_data:
                 out = validation_step(val_batch)
                 val_outs.append(out)
-                validation_epoch_end(val_outs)
+            validation_epoch_end(val_outs)
 
         Args:
             batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
                 The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
             batch_idx (int): The index of this batch
             dataloader_idx (int): The index of the dataloader that produced this batch
-                (only if multiple val datasets used)
+                (only if multiple val dataloaders used)
 
         Return:
            Any of.
@@ -675,11 +675,11 @@ class LightningModule(
                 # log the outputs!
                 self.log_dict({'val_loss': loss, 'val_acc': val_acc})
 
-        If you pass in multiple val datasets, validation_step will have an additional argument.
+        If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument.
 
         .. code-block:: python
 
-            # CASE 2: multiple validation datasets
+            # CASE 2: multiple validation dataloaders
             def validation_step(self, batch, batch_idx, dataloader_idx):
                 # dataloader_idx tells you which dataset this is.
 
@@ -811,7 +811,7 @@ class LightningModule(
                 The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
             batch_idx (int): The index of this batch.
             dataloader_idx (int): The index of the dataloader that produced this batch
-                (only if multiple test datasets used).
+                (only if multiple test dataloaders used).
 
         Return:
            Any of.
@@ -850,17 +850,16 @@ class LightningModule(
                 # log the outputs!
                 self.log_dict({'test_loss': loss, 'test_acc': test_acc})
 
-        If you pass in multiple validation datasets, :meth:`test_step` will have an additional
-        argument.
+        If you pass in multiple test dataloaders, :meth:`test_step` will have an additional argument.
 
         .. code-block:: python
 
-            # CASE 2: multiple test datasets
+            # CASE 2: multiple test dataloaders
             def test_step(self, batch, batch_idx, dataloader_idx):
                 # dataloader_idx tells you which dataset this is.
 
         Note:
-            If you don't need to validate you don't need to implement this method.
+            If you don't need to test you don't need to implement this method.
 
         Note:
             When the :meth:`test_step` is called, the model has been put in eval mode and