diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index f2b4c85123a9785024d15daa60702fb9bcd875dc..0903cddcf8c15a66d9ab3830bf6b1ff1c7229505 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -29,7 +29,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ["windows-latest", "ubuntu-latest", "macos-latest"]
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.8", "3.9", "3.10"]
     defaults:
       run:
         # somehow this also works for windows O.o ??
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 5e5ab6017378648b9a6ed3af91f72b921b17ed90..b8bd61cca7f80f763d7d61b77f84ac911de96e52 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -110,16 +110,16 @@ python310:
     reports:
       junit: report.xml
 
-python311:
-  stage: test
-  image: python:3.11
-  script:
-    - pytest tests -Werror --junitxml=report.xml
-    - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
-  artifacts:
-    when: always
-    reports:
-      junit: report.xml
+# python311:
+#   stage: test
+#   image: python:3.11
+#   script:
+#     - pytest tests -Werror --junitxml=report.xml
+#     - python -m saqc --config docs/resources/data/config.csv --data docs/resources/data/data.csv --outfile /tmp/test.csv
+#   artifacts:
+#     when: always
+#     reports:
+#       junit: report.xml
 
 doctest:
   stage: test
diff --git a/docs/cookbooks/CookBooksPanels.rst b/docs/cookbooks/CookBooksPanels.rst
index a852b237f492e98bd1329b6506d9b787ff54a8ad..981f8b4d80f689702239085ab55978d7c43727b4 100644
--- a/docs/cookbooks/CookBooksPanels.rst
+++ b/docs/cookbooks/CookBooksPanels.rst
@@ -15,6 +15,7 @@ Cook Books
    DataRegularisation
    OutlierDetection
    ResidualOutlierDetection
+   DriftDetection
    MultivariateFlagging
    ../documentation/GenericFunctions
 
@@ -63,6 +64,16 @@ Cook Books
       +++
       *Wrap your custom logical and arithmetic expressions with the generic functions*
 
+
+   .. grid-item-card:: Drift Detection
+      :link: DriftDetection
+      :link-type: doc
+
+      * define metrics to measure distance between data series
+      * automatically determine majority and anomalous data groups
+      +++
+      *Detecting datachunks drifting apart from a reference group*
+
    .. grid-item-card:: Modelling, Residuals and Arithmetics
       :link: ResidualOutlierDetection
       :link-type: doc
diff --git a/docs/cookbooks/DriftDetection.rst b/docs/cookbooks/DriftDetection.rst
new file mode 100644
index 0000000000000000000000000000000000000000..145247e74529845c9224c94b61a98f18a5558cbe
--- /dev/null
+++ b/docs/cookbooks/DriftDetection.rst
@@ -0,0 +1,233 @@
+.. SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+..
+.. SPDX-License-Identifier: GPL-3.0-or-later
+
+
+Drift Detection
+===============
+
+
+
+Overview
+--------
+
+The guide briefly introduces the usage of the :py:meth:`~saqc.SaQC.flagDriftFromNorm` method.
+The method detects sections in timeseries that deviate from the majority in a group of variables
+
+
+* :ref:`Parameters <cookbooks/DriftDetection:Parameters>`
+* :ref:`Algorithm <cookbooks/DriftDetection:Algorithm>`
+* :ref:`Example Data import <cookbooks/DriftDetection:Example Data import>`
+* :ref:`Example Algorithm Application <cookbooks/DriftDetection:Example Algorithm Application>`
+
+
+
+
+Parameters
+----------
+
+Although there seems to be a lot of user input to parametrize, most of it is easy to be interpreted and can be selected
+defaultly.
+
+window
+^^^^^^
+
+Length of the partitions the target group of data series` is divided into.
+For example, if selected ``1D`` (one day), the group to check will be divided into one day chunks and every chunk is be checked for time series deviating from the normal group.
+
+frac
+^^^^
+
+The percentage of data, needed to define the "normal" group expressed in a number out of :math:`[0,1]`.
+This, of course must be something over 50  percent (math:`0.5`), and can be
+selected according to the number of drifting variables one expects the data to have at max.
+
+method
+^^^^^^
+
+The linkage method can have some impact on the clustering, but sticking to the default value `single` might be
+sufficient for most the tasks.
+
+spread
+^^^^^^
+
+The main parameter to control the algorithm's behavior. It has to be selected carefully.
+It determines the maximum spread of a normal group by limiting the costs, a cluster agglomeration must not exceed in
+every linkage step.
+
+For singleton clusters, that costs equals half the distance, the timeseries in the clusters have to each other. So, only timeseries with a distance of less than two times the spreading norm can be clustered.
+
+When timeseries get clustered together, this new clusters distance to all the other timeseries/clusters is calculated
+according to the linkage method specified. By default, it is the minimum distance, the members of the clusters have to
+each other.
+
+Having that in mind, it is advisable to choose a distance function as metric, that can be well interpreted in the units
+dimension of the measurement, and where the interpretation is invariant over the length of the timeseries.
+
+metric
+^^^^^^
+
+The default *averaged manhatten metric* roughly represents the averaged value distance of two timeseries (as opposed to *euclidean*, which scales non linearly with the
+compared timeseries' length). For the selection of the :py:attr:`spread` parameter the default metric is helpful, since it allows to interpret the spreading in the dimension of the measurements.
+
+
+Algorithm
+---------
+
+The aim of the algorithm is to flag sections in timeseries, that significantly deviate from a normal group of timeseries running in parallel within a given section.
+
+"Normality" is determined in terms of a maximum spreading distance, that members of a normal group must not exceed.
+In addition, a group is only considered to be "normal", if it contains more then a certain percentage of the timeseries to be clustered into "normal" ones and "abnormal" ones.
+
+The steps of the algorithm are the following:
+
+* Calculate the distances :math:`d(x_i,x_j)` for all timeseries :math:`x_i` that are to be clustered with a metric specified by the user
+* Calculate a dendogram using a hierarchical linkage algorithm, specified by the user.
+* Flatten the dendogram at the level, the agglomeration costs exceed the value given by a spreading norm, specified by the user
+* check if there is a cluster containing more than a certain percentage of variables as specified by the user.
+   * if yes: flag all the variables that are not in that cluster
+   * if no: flag nothing
+
+Example Data Import
+-------------------
+
+.. plot::
+   :context: reset
+   :include-source: False
+
+   import matplotlib
+   import saqc
+   import pandas as pd
+   data = pd.read_csv('../resources/data/tempSensorGroup.csv', index_col=0)
+   data.index = pd.DatetimeIndex(data.index)
+   qc = saqc.SaQC(data)
+
+We load the example `data set <https://git.ufz.de/rdm-software/saqc/-/blob/develop/docs/resources/data/tempsenorGroup.csv>`_
+from the *saqc* repository using the `pandas <https://pandas.pydata.org/>`_ csv
+file reader. Subsequently, we cast the index of the imported data to `DatetimeIndex`
+and use the dataframe's `plot` method, to inspect the imported data:
+
+.. doctest:: flagDriftFromNorm
+
+   >>> data = pd.read_csv('./resources/data/tempSensorGroup.csv', index_col=0)
+   >>> data.index = pd.DatetimeIndex(data.index)
+   >>> data.plot() # doctest: +SKIP
+
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+    data.plot()
+
+
+Example Algorithm Application
+-----------------------------
+
+Looking at our example data set more closely, we see that 2 of the 5 variables start to drift away.
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+   :caption: 2 variables start departing the majority group of variables (the group containing more than ``frac`` variables) around july.
+
+    data['2017-05':'2017-11'].plot()
+
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+   :caption: 2 variables are departed from the majority group of variables (the group containing more than ``frac`` variables) by the end of the year.
+
+    data['2017-09':'2018-01'].plot()
+
+Lets try to detect those drifts via saqc. There for we import the *saqc* package and instantiate a :py:class:`saqc.SaQC`
+object with the data:
+
+.. doctest:: flagDriftFromNorm
+
+   >>> import saqc
+   >>> qc = saqc.SaQC(data)
+
+The changes we observe in the data seem to develop significantly only in temporal spans over a month,
+so we go for ``"1M"`` as value for the
+``window`` parameter. We identified the majority group as a group containing three variables, whereby two variables
+seem to be scattered away, so that we can leave the ``frac`` value at its default ``.5`` level.
+The majority group seems on average not to be spread out more than 3 or 4 degrees. So, for the ``spread`` value
+we go for ``3``. This can be interpreted as follows, for every member of a group, there is another member that
+is not distanted more than ``3`` degrees from that one (on average in one month) - this should be sufficient to bundle
+the majority group and to discriminate against the drifting variables, that seem to deviate more than 3 degrees on
+average in a month from any member of the majority group.
+
+.. doctest:: flagDriftFromNorm
+
+   >>> variables = ['temp1 [degC]', 'temp2 [degC]', 'temp3 [degC]', 'temp4 [degC]', 'temp5 [degC]']
+   >>> qc = qc.flagDriftFromNorm(variables, window='1M', spread=3)
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   >>> variables = ['temp1 [degC]', 'temp2 [degC]', 'temp3 [degC]', 'temp4 [degC]', 'temp5 [degC]']
+   >>> qc = qc.flagDriftFromNorm(variables, window='1M', spread=3)
+
+Lets check the results:
+
+.. doctest:: flagDriftFromNorm
+
+   >>> qc.plot('temp1 [degC]') # doctest: +SKIP
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   qc.plot('temp1 [degC]')
+
+.. doctest:: flagDriftFromNorm
+
+   >>> qc.plot('temp2 [degC]') # doctest: +SKIP
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   qc.plot('temp2 [degC]')
+
+.. doctest:: flagDriftFromNorm
+
+   >>> qc.plot('temp3 [degC]') # doctest: +SKIP
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   qc.plot('temp3 [degC]')
+
+.. doctest:: flagDriftFromNorm
+
+   >>> qc.plot('temp4 [degC]') # doctest: +SKIP
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   qc.plot('temp4 [degC]')
+
+.. doctest:: flagDriftFromNorm
+
+   >>> qc.plot('temp5 [degC]') # doctest: +SKIP
+
+.. plot::
+   :context: close-figs
+   :include-source: False
+   :class: center
+
+   qc.plot('temp5 [degC]')
\ No newline at end of file
diff --git a/docs/misc/title.rst b/docs/misc/title.rst
index 7bfc81830ac53a6668d2701aff516d3bd09eb4d2..09a2dbe8c03684f38fc1f35771908457016cc6dc 100644
--- a/docs/misc/title.rst
+++ b/docs/misc/title.rst
@@ -62,6 +62,7 @@ Documentation
 
       * outlier detection
       * frequency alignment
+      * drift detection
       * data modelling
       * wrapping generic or custom functionality
       +++
diff --git a/docs/resources/data/tempSensorGroup.csv b/docs/resources/data/tempSensorGroup.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8ba26d4afd43aca5cdee0b8dca79c0f5a6f53972
--- /dev/null
+++ b/docs/resources/data/tempSensorGroup.csv
@@ -0,0 +1,52560 @@
+,temp1 [degC],temp2 [degC],temp3 [degC],temp4 [degC],temp5 [degC]
+2017-01-01 00:10:00,2.265,1.412,2.5660000000000003,2.506,3.425
+2017-01-01 00:20:00,2.267,1.4269999999999998,2.567,2.516,3.388
+2017-01-01 00:30:00,2.272,1.431,2.563,2.524,3.363
+2017-01-01 00:40:00,2.287,1.4469999999999998,2.5810000000000004,2.532,3.3560000000000003
+2017-01-01 00:50:00,2.298,1.464,2.611,2.546,3.377
+2017-01-01 01:00:00,2.316,1.473,2.659,2.56,3.442
+2017-01-01 01:10:00,2.326,1.4880000000000002,2.7060000000000004,2.574,3.522
+2017-01-01 01:20:00,2.342,1.496,2.748,2.585,3.604
+2017-01-01 01:30:00,2.356,1.508,2.784,2.605,3.675
+2017-01-01 01:40:00,2.373,1.523,2.82,2.6260000000000003,3.75
+2017-01-01 01:50:00,2.393,1.538,2.862,2.651,3.832
+2017-01-01 02:00:00,2.411,1.5519999999999998,2.901,2.6710000000000003,3.905
+2017-01-01 02:10:00,2.4290000000000003,1.567,2.931,2.695,3.97
+2017-01-01 02:20:00,2.447,1.582,2.959,2.7230000000000003,4.023
+2017-01-01 02:30:00,2.466,1.597,2.978,2.7430000000000003,4.077
+2017-01-01 02:40:00,2.48,1.6130000000000002,2.991,2.769,4.1
+2017-01-01 02:50:00,2.495,1.622,2.998,2.792,4.107
+2017-01-01 03:00:00,2.504,1.638,2.979,2.8080000000000003,4.065
+2017-01-01 03:10:00,2.514,1.648,2.946,2.826,3.968
+2017-01-01 03:20:00,2.514,1.656,2.886,2.833,3.848
+2017-01-01 03:30:00,2.51,1.658,2.813,2.839,3.684
+2017-01-01 03:40:00,2.496,1.658,2.73,2.83,3.477
+2017-01-01 03:50:00,2.479,1.658,2.63,2.825,3.23
+2017-01-01 04:00:00,2.461,1.662,2.528,2.8080000000000003,2.951
+2017-01-01 04:10:00,2.44,1.662,2.436,2.79,2.692
+2017-01-01 04:20:00,2.417,1.662,2.345,2.767,2.463
+2017-01-01 04:30:00,2.397,1.663,2.263,2.737,2.259
+2017-01-01 04:40:00,2.374,1.664,2.184,2.702,2.07
+2017-01-01 04:50:00,2.347,1.661,2.11,2.665,1.875
+2017-01-01 05:00:00,2.324,1.6469999999999998,2.043,2.6260000000000003,1.727
+2017-01-01 05:10:00,2.296,1.636,1.976,2.583,1.587
+2017-01-01 05:20:00,2.264,1.625,1.902,2.535,1.46
+2017-01-01 05:30:00,2.233,1.609,1.83,2.489,1.329
+2017-01-01 05:40:00,2.205,1.596,1.76,2.443,1.204
+2017-01-01 05:50:00,2.176,1.58,1.6909999999999998,2.393,1.083
+2017-01-01 06:00:00,2.137,1.5590000000000002,1.611,2.341,0.936
+2017-01-01 06:10:00,2.103,1.54,1.529,2.286,0.794
+2017-01-01 06:20:00,2.067,1.521,1.449,2.234,0.6509999999999999
+2017-01-01 06:30:00,2.035,1.501,1.374,2.1790000000000003,0.52
+2017-01-01 06:40:00,1.998,1.483,1.301,2.124,0.4029999999999999
+2017-01-01 06:50:00,1.961,1.459,1.225,2.066,0.295
+2017-01-01 07:00:00,1.923,1.4369999999999998,1.15,2.006,0.185
+2017-01-01 07:10:00,1.888,1.412,1.081,1.948,0.077
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+2017-01-01 07:30:00,1.811,1.367,0.939,1.829,-0.127
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+2017-12-31 23:20:00,20.11,7.106,16.04,13.18,11.07
+2017-12-31 23:30:00,20.13,7.132000000000001,16.05,13.22,11.08
+2017-12-31 23:40:00,20.15,7.153,16.07,13.25,11.08
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diff --git a/docs/resources/data/tempSensorGroup.csv.license b/docs/resources/data/tempSensorGroup.csv.license
new file mode 100644
index 0000000000000000000000000000000000000000..f8c6bf8cd36fb9a9a0a0dd474407f40908bf5d1f
--- /dev/null
+++ b/docs/resources/data/tempSensorGroup.csv.license
@@ -0,0 +1,3 @@
+SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+
+SPDX-License-Identifier: GPL-3.0-or-later
\ No newline at end of file
diff --git a/saqc/funcs/drift.py b/saqc/funcs/drift.py
index 1e6fa1e235f38959a7b6225ec0003b738e0429e6..98c2b335271721f8a3b294a58476359d9eff3b63 100644
--- a/saqc/funcs/drift.py
+++ b/saqc/funcs/drift.py
@@ -10,6 +10,7 @@ from __future__ import annotations
 
 import functools
 import inspect
+import warnings
 from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple
 
 import numpy as np
@@ -22,7 +23,8 @@ from saqc import BAD
 from saqc.core import DictOfSeries, Flags, flagging, register
 from saqc.funcs.changepoints import _getChangePoints
 from saqc.lib.docs import DOC_TEMPLATES
-from saqc.lib.tools import detectDeviants, filterKwargs, toSequence
+from saqc.lib.exceptions import ParameterOutOfBounds
+from saqc.lib.tools import detectDeviants, filterKwargs, isInBounds, toSequence
 from saqc.lib.ts_operators import expDriftModel, linearDriftModel
 from saqc.lib.types import CurveFitter
 
@@ -53,7 +55,7 @@ class DriftMixin:
     def flagDriftFromNorm(
         self: "SaQC",
         field: Sequence[str],
-        freq: str,
+        window: str,
         spread: float,
         frac: float = 0.5,
         metric: Callable[
@@ -74,7 +76,7 @@ class DriftMixin:
 
         Parameters
         ----------
-        freq :
+        window :
             Frequency, that split the data in chunks.
 
         spread :
@@ -130,12 +132,25 @@ class DriftMixin:
         Introduction to Hierarchical clustering:
             [2] https://en.wikipedia.org/wiki/Hierarchical_clustering
         """
+        if not isInBounds(frac, (0.5, 1), closed="both"):
+            raise ParameterOutOfBounds(frac, "frac", (0.5, 1), "both")
+
+        if "freq" in kwargs:
+            warnings.warn(
+                """
+                The parameter `freq` is deprecated and will be removed in version 3.0 of saqc.
+                Please us the parameter `window` instead.'
+                """,
+                DeprecationWarning,
+            )
+            window = kwargs["freq"]
+
         fields = toSequence(field)
 
         data = self._data[fields].to_pandas()
         data.dropna(inplace=True)
 
-        segments = data.groupby(pd.Grouper(freq=freq))
+        segments = data.groupby(pd.Grouper(freq=window))
         for segment in segments:
             if len(segment[1]) <= 1:
                 continue
diff --git a/saqc/lib/exceptions.py b/saqc/lib/exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b8748fd3c285826a84cbb986539f92700676ba3
--- /dev/null
+++ b/saqc/lib/exceptions.py
@@ -0,0 +1,50 @@
+#! /usr/bin/env python
+
+# SPDX-FileCopyrightText: 2021 Helmholtz-Zentrum für Umweltforschung GmbH - UFZ
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+# -*- coding: utf-8 -*-
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    List,
+    Literal,
+    Sequence,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+CLOSURE_TO_NOTION = {
+    None: "interval ({}, {})",
+    "left": "right-open interval [{}, {})]",
+    "right": "left-open interval ({}, {}]",
+    "both": "closed interval [{}, {}]",
+}
+
+
+class ParameterOutOfBounds(Exception):
+    def __init__(
+        self,
+        value: int | float,
+        para_name: str,
+        bounds: Tuple[str],
+        closed: Literal["right", "left", "both"] = None,
+    ):
+        Exception.__init__(self)
+        self.value = value
+        self.para_name = para_name
+        self.bounds = bounds
+        self.closed = closed
+        self.msg = "Parameter '{}' has to be in the {}, but {} was passed."
+
+    def __str__(self):
+        return self.msg.format(
+            self.para_name,
+            CLOSURE_TO_NOTION[self.closed].format(self.bounds[0], self.bounds[1]),
+            self.value,
+        )
diff --git a/saqc/lib/tools.py b/saqc/lib/tools.py
index 4097f36dca35c1b5bbd7aedd06925617b7e701ea..5c719aa535c72433753f973c6f15720fb601931f 100644
--- a/saqc/lib/tools.py
+++ b/saqc/lib/tools.py
@@ -10,9 +10,20 @@ from __future__ import annotations
 import collections
 import functools
 import itertools
+import operator as op
 import re
 import warnings
-from typing import Any, Callable, Collection, List, Sequence, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    Collection,
+    List,
+    Literal,
+    Sequence,
+    Tuple,
+    TypeVar,
+    Union,
+)
 
 import numpy as np
 import pandas as pd
@@ -22,6 +33,40 @@ from scipy.cluster.hierarchy import fcluster, linkage
 from saqc.lib.types import CompT
 
 T = TypeVar("T", str, float, int)
+BOUND_OPERATORS = {
+    None: (op.le, op.ge),
+    "both": (op.lt, op.gt),
+    "right": (op.le, op.gt),
+    "left": (op.gt, op.le),
+}
+
+
+def isInBounds(
+    val: int | float,
+    bounds: Tuple[int | float],
+    closed: Literal["left", "right", "both"] = None,
+):
+    """
+    check if val falls into the interval [left, right] and return boolean accordingly
+
+    val :
+        value to check
+
+    bounds :
+        Tuple containing left and right interval bounds. Pass `(a, b)` to define the interval [`a`, `b`].
+        Set `a=-inf` or `b=+inf` to set one sided restriction.
+
+    closed :
+        Enclosure includes the interval bounds into the constraint interval. By default, the bounds
+        are not included. Pass:
+        * `"both"`: to include both sides of the interval
+        * `"left"`: to include left bound
+        * `"right"`: to include right bound
+    """
+    ops = BOUND_OPERATORS[closed]
+    if ops[0](val, bounds[0]) or ops[1](val, bounds[1]):
+        return False
+    return True
 
 
 def assertScalar(name, value, optional=False):
@@ -253,7 +298,6 @@ def estimateFrequency(
 
     len_f = len(delta_f) * 2
     min_energy = delta_f[0] * min_energy
-    # calc/assign low/high freq cut offs (makes life easier):
     min_rate_i = int(
         len_f / (pd.Timedelta(min_rate).total_seconds() * (10**delta_precision))
     )
@@ -383,7 +427,7 @@ def getFreqDelta(index: pd.Index) -> None | pd.Timedelta:
     (``None`` will also be returned for pd.RangeIndex type.)
 
     """
-    delta = getattr(index, "freq", None)
+    delta = getattr(index, "window", None)
     if delta is None and not index.empty:
         i = pd.date_range(index[0], index[-1], len(index))
         if i.equals(index):
diff --git a/tests/common.py b/tests/common.py
index e9375c73e77a10c4add8616e80f6c37f494939cf..cdf418c2327c8f3255f42cbd9f5507cec464069a 100644
--- a/tests/common.py
+++ b/tests/common.py
@@ -88,7 +88,7 @@ def checkInvariants(data, flags, field, identical=True):
 
     assert flags[field].dtype == float
 
-    # `pd.Index.identical` also check index attributes like `freq`
+    # `pd.Index.identical` also check index attributes like `window`
     if identical:
         assert data[field].index.identical(flags[field].index)
     else:
diff --git a/tests/funcs/test_functions.py b/tests/funcs/test_functions.py
index 5ac707b351058d7dcbb6b5556fa9a4a8ddaf6336..3a8a696a479fd3d81cbc507ba9d5361a7349b028 100644
--- a/tests/funcs/test_functions.py
+++ b/tests/funcs/test_functions.py
@@ -253,7 +253,7 @@ def test_flagDriftFromNorm(dat):
     flags = initFlagsLike(data)
     qc = SaQC(data, flags).flagDriftFromNorm(
         field=fields,
-        freq="200min",
+        window="200min",
         spread=5,
         flag=BAD,
     )