diff --git a/src/elisa/data/ogip.py b/src/elisa/data/ogip.py
index 6d9c7d4c..8ca7e9a0 100644
--- a/src/elisa/data/ogip.py
+++ b/src/elisa/data/ogip.py
@@ -54,18 +54,21 @@ class Data:
         available so that each channel group has:
 
             * ``'const'``: `scale` number channels
-            * ``'min'``: counts >= `scale` for src + bkg
-            * ``'sig'``: src significance >= `scale`-sigma
+            * ``'min'``: total (source + background) counts >= `scale`
+            * ``'sig'``: source significance >= `scale` sigma
             * ``'opt'``: optimal binning, see Kaastra & Bleeker (2016) [3]_
-            * ``'optmin'``: opt with counts >= `scale` for src + bkg
-            * ``'optsig'``: opt with src significance >= `scale`-sigma
-            * ``'bmin'``: counts >= `scale` for bkg, useful for ``wstat``
-            * ``'bpos'``: bkg < 0 with probability < `scale`, useful for
-              ``pgstat``
+            * ``'optmin'``: optimal binning with total counts >= `scale`
+            * ``'optsig'``: optimal binning with source significance >= `scale`
+              sigma
+            * ``'bmin'``: background counts >= `scale`, used to avoid bias when
+              using ``wstat`` to simultaneously fit the source and background
+            * ``'bpos'``: background counts < 0 with probability < `scale`,
+              used to avoid bias when using ``pgstat`` to simultaneously fit
+              the source and background
 
         The default is None.
     scale : float or None, optional
-        Grouping scale. Only takes effect if `group` is not None.
+        Grouping scale for the method specified in `group`.
     spec_poisson : bool or None, optional
         Whether the spectrum data follows counting statistics, reading from
         the `specfile` header. This value must be set if ``POISSERR`` is
@@ -288,14 +291,17 @@ def group(self, method: str, scale: float | int):
             are available so that each channel group has:
 
             * ``'const'``: `scale` number channels
-            * ``'min'``: counts >= `scale` for src + bkg
-            * ``'sig'``: src significance >= `scale`-sigma
-            * ``'opt'``: optimal binning, see Kaastra & Bleeker (2016) [1]_
-            * ``'optmin'``: opt with counts >= `scale` for src + bkg
-            * ``'optsig'``: opt with src significance >= `scale`-sigma
-            * ``'bmin'``: counts >= `scale` for bkg (useful for ``wstat``)
-            * ``'bpos'``: bkg < 0 with probability < `scale`, useful for
-              ``pgstat``
+            * ``'min'``: total (source + background) counts >= `scale`
+            * ``'sig'``: source significance >= `scale` sigma
+            * ``'opt'``: optimal binning, see Kaastra & Bleeker (2016) [3]_
+            * ``'optmin'``: optimal binning with total counts >= `scale`
+            * ``'optsig'``: optimal binning with source significance >= `scale`
+              sigma
+            * ``'bmin'``: background counts >= `scale`, used to avoid bias when
+              using ``wstat`` to simultaneously fit the source and background
+            * ``'bpos'``: background counts < 0 with probability < `scale`,
+              used to avoid bias when using ``pgstat`` to simultaneously fit
+              the source and background
 
         scale : float
             Grouping scale.
diff --git a/src/elisa/util/misc.py b/src/elisa/util/misc.py
index 02d932e6..c37480e3 100644
--- a/src/elisa/util/misc.py
+++ b/src/elisa/util/misc.py
@@ -200,49 +200,6 @@ def make_pretty_table(fields: Sequence[str], rows: Sequence) -> PrettyTable:
     return table
 
 
-# def ordered_composition(
-#     basic_mapping: Sequence[ParamID],
-#     composite_mapping: dict[ParamID, Sequence[ParamID]],
-# ) -> tuple[ParamID, ...]:
-#     """Get ordered composition.
-#
-#     Parameters
-#     ----------
-#     basic_mapping : sequence
-#         Sequence of basic parameter id.
-#     composite_mapping : dict
-#         Mapping of composite parameter id to sequence of parameter id, which
-#         may include id existing in `composite_mapping`.
-#
-#     Returns
-#     -------
-#     ordered : tuple
-#         Ordered keys of `composite_mapping`.
-#
-#     """
-#     nodes = set(i for pids in composite_mapping.values() for i in pids)
-#     available_nodes = set(basic_mapping) | set(composite_mapping)
-#     if nodes - available_nodes != set():
-#         raise ValueError(
-#             '`composition` id must be a subset of union of `basic_mapping` '
-#             'and `composition`'
-#         )
-#
-#     ordered = list(basic_mapping)
-#     remains = dict(composite_mapping)
-#     while remains:
-#         to_del = []
-#         for k, v in remains:
-#             if all(pid in ordered for pid in v):
-#                 to_del.append(k)
-#             ordered.append(k)
-#
-#         for k in to_del:
-#             del remains[k]
-#
-#     return tuple(k for k in ordered if k not in basic_mapping)
-
-
 def replace_string(value: T, mapping: dict[str, str]) -> T:
     """Replace all strings in `value` appeared in `mapping`.