diff --git a/saucebrush/stats.py b/saucebrush/stats.py
index 6f115d1..b726035 100644
--- a/saucebrush/stats.py
+++ b/saucebrush/stats.py
@@ -1,13 +1,67 @@
 from saucebrush.filters import Filter
+import itertools
+import math
+
+def _average(values):
+    """ Calculate the average of a list of values.
+    
+        :param values: an iterable of ints or floats to average
+    """
+    return sum(values) / float(len(values))
+
+def _median(values):
+    """ Calculate the median of a list of values.
+    
+        :param values: an iterable of ints or floats to calculate
+    """
+    
+    count = len(values)
+    
+    # bail early before sorting if 0 or 1 values in list
+    if count == 0:
+        return None
+    elif count == 1:
+        return values[0]
+    
+    values = sorted(values)
+    
+    if count % 2 == 1:
+        # odd number of items, return middle value
+        return values[count / 2]
+    else:
+        # even number of items, return average of middle two items
+        mid = count / 2
+        return sum(values[mid - 1:mid + 1]) / 2.0
+
+def _stddev(values, population=False):
+    """ Calculate the standard deviation and variance of a list of values.
+    
+        :param values: an iterable of ints or floats to calculate
+        :param population: True if values represents entire population,
+            False if it is a sample of the population
+    """
+    
+    avg = _average(values)
+    count = len(values) if population else len(values) - 1
+    
+    # square the difference between each value and the average
+    diffsq = ((i - avg) ** 2 for i in values)
+    
+    # the average of the squared differences
+    variance = sum(diffsq) / float(count)
+    
+    return (math.sqrt(variance), variance) # stddev is sqrt of variance
 
 class StatsFilter(Filter):
+    """ Base for all stats filters.
+    """
     
     def __init__(self, field, test=None):
         self._field = field
-        self._test = test if test else lambda x: True
+        self._test = test
         
     def process_record(self, record):
-        if self._test(record):
+        if self._test is None or self._test(record):
             self.process_field(record[self._field])
         return record
     
@@ -20,6 +74,9 @@ class StatsFilter(Filter):
                                   self.__class__.__name__)
 
 class Sum(StatsFilter):
+    """ Calculate the sum of the values in a field. Field must contain either
+        int or float values.
+    """
     
     def __init__(self, field, initial=0, **kwargs):
         super(Sum, self).__init__(field, **kwargs)
@@ -32,6 +89,9 @@ class Sum(StatsFilter):
         return self._value
 
 class Average(StatsFilter):
+    """ Calculate the average (mean) of the values in a field. Field must
+        contain either int or float values.
+    """
     
     def __init__(self, field, initial=0, **kwargs):
         super(Average, self).__init__(field, **kwargs)
@@ -39,8 +99,79 @@ class Average(StatsFilter):
         self._count = 0
         
     def process_field(self, item):
-        self._value += item or 0
-        self._count += 1
+        if item is not None:
+            self._value += item
+            self._count += 1
         
     def value(self):
-        return self._value / self._count
\ No newline at end of file
+        return self._value / float(self._count)
+
+class Median(StatsFilter):
+    """ Calculate the median of the values in a field. Field must contain
+        either int or float values.
+        
+        **This filter keeps a list of field values in memory.**
+    """
+    
+    def __init__(self, field, **kwargs):
+        super(Median, self).__init__(field, **kwargs)
+        self._values = []
+    
+    def process_field(self, item):
+        if item is not None:
+            self._values.append(item)
+    
+    def value(self):
+        return _median(self._values)
+
+class MinMax(StatsFilter):
+    """ Find the minimum and maximum values in a field. Field must contain
+        either int or float values.
+    """
+    
+    def __init__(self, field, **kwargs):
+        super(MinMax, self).__init__(field, **kwargs)
+        self._max = None
+        self._min = None
+    
+    def process_field(self, item):
+        if item is not None:
+            if self._max is None or item > self._max:
+                self._max = item
+            if self._min is None or item < self._min:
+                self._min = item
+    
+    def value(self):
+        return (self._min, self._max)
+
+class StandardDeviation(StatsFilter):
+    """ Calculate the standard deviation of the values in a field. Calling
+        value() will return a standard deviation for the sample. Pass
+        population=True to value() for the standard deviation of the
+        population. Convenience methods are provided for average() and
+        median(). Field must contain either int or float values.
+
+        **This filter keeps a list of field values in memory.**
+    """
+    
+    def __init__(self, field, **kwargs):
+        super(StandardDeviation, self).__init__(field, **kwargs)
+        self._values = []
+    
+    def process_field(self, item):
+        if item is not None:
+            self._values.append(item)
+    
+    def average(self):
+        return _average(self._values)
+    
+    def median(self):
+        return _median(self._values)
+    
+    def value(self, population=False):
+        """ Return a tuple of (standard_deviation, variance).
+            
+            :param population: True if values represents entire population,
+                False if values is a sample. Default: False
+        """
+        return _stddev(self._values, population)
\ No newline at end of file