diff --git a/stdlib/3.4/statistics.pyi b/stdlib/3.4/statistics.pyi
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+# Stubs for statistics
+# See: http://docs.python.org/3/library/statistics.html
+
+import sys
+from typing import Iterable, Iterator, Sequence, TypeVar, Union, overload
+from decimal import Decimal
+
+# Note: according to the docs, statistics only explicitly supports
+# int, float, Decimal, and Fraction. Other types within the numeric
+# tower are not supported. It also states mixing together different
+# types results in undefined behavior, so the type signatures below
+# deliberately enforce that numeric types may not be mixed.
+#
+# TODO: Once either https://github.com/python/typeshed/pull/545 or
+# https://github.com/python/typeshed/pull/94 is accepted, add support
+# for fractions.
+_TNum = TypeVar('_TNum', int, float, Decimal)
+_T = TypeVar('_T')
+
+def mean(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> _TNum: ...
+if sys.version_info >= (3, 6):
+    def geometric_mean(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> _TNum: ...
+    def harmonic_mean(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> _TNum: ...
+
+# Note: in CPython, the output of median_grouped may sometimes be coerced to
+# float as an implementation detail. In the interests of not breaking code
+# that relies on this implementation detail, the return type is set to the
+# Union of float and the contained numeric type.
+def median(data: Iterable[_TNum]) -> _TNum: ...
+def median_low(data: Iterable[_TNum]) -> _TNum: ...
+def median_high(data: Iterable[_TNum]) -> _TNum: ...
+# TODO: interval should also accept a Fraction.
+def median_grouped(data: Iterable[_TNum],
+                   interval: Union[int, float] = 1
+                   ) -> Union[_TNum, float]: ...
+
+def mode(data: Iterable[_T]) -> _T: ...
+
+def pstdev(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> Union[float, Decimal]: ...
+def stdev(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> Union[float, Decimal]: ...
+def pvariance(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> _TNum: ...
+def variance(data: Union[Iterator[_TNum], Sequence[_TNum]]) -> _TNum: ...
+
+class StatisticsError(ValueError): pass