diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index a7ec65c7..49f46614 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -9,23 +9,6 @@ Pandas supports all these `BigQuery data types <https://cloud.google.com/bigquer
 ``TIMESTAMP`` (microsecond precision). Data types ``BYTES`` and ``RECORD``
 are not supported.
 
-Integer and boolean ``NA`` handling
-+++++++++++++++++++++++++++++++++++
-
-Since all columns in BigQuery queries are nullable, and NumPy lacks of ``NA``
-support for integer and boolean types, this module will store ``INTEGER`` or
-``BOOLEAN`` columns with at least one ``NULL`` value as ``dtype=object``.
-Otherwise those columns will be stored as ``dtype=int64`` or ``dtype=bool``
-respectively.
-
-This is opposite to default pandas behaviour which will promote integer
-type to float in order to store NAs.
-`See here for how this works in pandas <https://pandas.pydata.org/pandas-docs/stable/gotchas.html#nan-integer-na-values-and-na-type-promotions>`__
-
-While this trade-off works well for most cases, it breaks down for storing
-values greater than 2**53. Such values in BigQuery can represent identifiers
-and unnoticed precision lost for identifier is what we want to avoid.
-
 Logging
 +++++++
 
diff --git a/docs/source/reading.rst b/docs/source/reading.rst
index b188c392..add61ed2 100644
--- a/docs/source/reading.rst
+++ b/docs/source/reading.rst
@@ -50,6 +50,7 @@ your job. For more information about query configuration parameters see `here
 .. note::
 
     The ``dialect`` argument can be used to indicate whether to use BigQuery's ``'legacy'`` SQL
-    or BigQuery's ``'standard'`` SQL (beta). The default value is ``'legacy'``. For more information
+    or BigQuery's ``'standard'`` SQL (beta). The default value is ``'legacy'``, though this will change
+    in a subsequent release to ``'standard'``. For more information
     on BigQuery's standard SQL, see `BigQuery SQL Reference
     <https://cloud.google.com/bigquery/sql-reference/>`__
diff --git a/docs/source/tables.rst b/docs/source/tables.rst
index ba07125d..dcf891ee 100644
--- a/docs/source/tables.rst
+++ b/docs/source/tables.rst
@@ -14,9 +14,3 @@ Creating Tables
             {'name': 'my_int64', 'type': 'INTEGER'},
             {'name': 'my_string', 'type': 'STRING'}]}
 
-.. note::
-
-   If you delete and re-create a BigQuery table with the same name, but different table schema,
-   you must wait 2 minutes before streaming data into the table. As a workaround, consider creating
-   the new table with a different name. Refer to
-   `Google BigQuery issue 191 <https://code.google.com/p/google-bigquery/issues/detail?id=191>`__.
diff --git a/docs/source/writing.rst b/docs/source/writing.rst
index 14124d03..e649d7fd 100644
--- a/docs/source/writing.rst
+++ b/docs/source/writing.rst
@@ -49,11 +49,6 @@ a ``TableCreationError`` if the destination table already exists.
    If an error occurs while streaming data to BigQuery, see
    `Troubleshooting BigQuery Errors <https://cloud.google.com/bigquery/troubleshooting-errors>`__.
 
-.. note::
-
-   The BigQuery SQL query language has some oddities, see the
-   `BigQuery Query Reference Documentation <https://cloud.google.com/bigquery/query-reference>`__.
-
 .. note::
 
    While BigQuery uses SQL-like syntax, it has some important differences