diff --git a/CHANGELOG.md b/CHANGELOG.md
index d34e52ee2..cd3a5e3a8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,10 +8,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 - `n_restarts` and `n_raw_samples` keywords to configure continuous optimization
   behavior for `BotorchRecommender`
+- User guide for utilities
+
+### Changed
+- Utility `add_fake_results` renamed to `add_fake_measurements`
+- Utilities `add_fake_measurements` and `add_parameter_noise` now also return the
+  dataframe they modified in-place
 
 ### Fixed
 - Leftover attrs-decorated classes are garbage collected before the subclass tree is
-  traversed, avoiding sporadic serialization problems 
+  traversed, avoiding sporadic serialization problems
 
 ## [0.11.1] - 2024-10-01
 ### Added
@@ -24,8 +30,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 - Unsafe name-based matching of columns in `get_comp_rep_parameter_indices`
-- Leftover attrs-decorated classes are garbage collected before the subclass tree is
-  traversed, avoiding sporadic serialization problems 
 
 ### Deprecations
 - `ContinuousLinearEqualityConstraint` and `ContinuousLinearInequalityConstraint`
diff --git a/baybe/simulation/lookup.py b/baybe/simulation/lookup.py
index e10368aab..f96c68e62 100644
--- a/baybe/simulation/lookup.py
+++ b/baybe/simulation/lookup.py
@@ -11,7 +11,7 @@
 
 from baybe.simulation._imputation import _impute_lookup
 from baybe.targets.base import Target
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 _logger = logging.getLogger(__name__)
 
@@ -68,7 +68,7 @@ def look_up_targets(
         2  3    15.0
     """
     if lookup is None:
-        add_fake_results(queries, targets)
+        add_fake_measurements(queries, targets)
     elif isinstance(lookup, Callable):
         _look_up_targets_from_callable(queries, targets, lookup)
     elif isinstance(lookup, pd.DataFrame):
diff --git a/baybe/utils/dataframe.py b/baybe/utils/dataframe.py
index 80d3c1c4a..e365c5bc3 100644
--- a/baybe/utils/dataframe.py
+++ b/baybe/utils/dataframe.py
@@ -66,19 +66,19 @@ def to_tensor(*x: np.ndarray | pd.DataFrame) -> Tensor | tuple[Tensor, ...]:
     return out
 
 
-def add_fake_results(
+def add_fake_measurements(
     data: pd.DataFrame,
     targets: Collection[Target],
     good_reference_values: dict[str, list] | None = None,
     good_intervals: dict[str, tuple[float, float]] | None = None,
     bad_intervals: dict[str, tuple[float, float]] | None = None,
-) -> None:
-    """Add fake results to a dataframe which was the result of a BayBE recommendation.
+) -> pd.DataFrame:
+    """Add fake measurements to a dataframe which was the result of a recommendation.
 
     It is possible to specify "good" values, which will be given a better
     target value. With this, the algorithm can be driven towards certain optimal values
-    whilst still being random. Useful for testing. Note that this does not return a
-    new dataframe and that the dataframe is changed in-place.
+    whilst still being random. Useful for testing. Note that the dataframe is changed
+    in-place and also returned.
 
     Args:
         data: A dataframe containing parameter configurations in experimental
@@ -99,6 +99,9 @@ def add_fake_results(
             the parameters lie outside the conditions specified through
             ``good_reference_values``.
 
+    Returns:
+        The modified dataframe.
+
     Raises:
         ValueError: If good values for a parameter were specified, but this parameter
             is not part of the dataframe.
@@ -216,19 +219,21 @@ def add_fake_results(
                 final_mask.sum(),
             )
 
+    return data
+
 
 def add_parameter_noise(
     data: pd.DataFrame,
     parameters: Iterable[Parameter],
     noise_type: Literal["absolute", "relative_percent"] = "absolute",
     noise_level: float = 1.0,
-) -> None:
+) -> pd.DataFrame:
     """Apply uniform noise to the parameter values of a recommendation frame.
 
     The noise can be additive or multiplicative.
     This can be used to simulate experimental noise or imperfect user input containing
     numerical parameter values that differ from the recommendations. Note that the
-    dataframe is modified in-place, and that no new dataframe is returned.
+    dataframe is changed in-place and also returned.
 
     Args:
         data: Output of the ``recommend`` function of a ``Campaign`` object, see
@@ -239,6 +244,9 @@ def add_parameter_noise(
             for noise type ``absolute`` and as percentage for noise type
             ``relative_percent``.
 
+    Returns:
+        The modified dataframe.
+
     Raises:
         ValueError: If ``noise_type`` is neither ``absolute`` nor
             ``relative_percent``.
@@ -265,6 +273,8 @@ def add_parameter_noise(
                 param.bounds.lower, param.bounds.upper
             )
 
+    return data
+
 
 def df_drop_single_value_columns(
     df: pd.DataFrame, lst_exclude: list = None
diff --git a/docs/userguide/async.md b/docs/userguide/async.md
index 177d4d772..337fd273b 100644
--- a/docs/userguide/async.md
+++ b/docs/userguide/async.md
@@ -65,6 +65,8 @@ Akin to `measurements` or `recommendations`, `pending_experiments` is a datafram
 In the following example, we get a set of recommendations, add results for half of them,
 and start the next recommendation, marking the other half pending:
 ```python
+from baybe.utils.dataframe import add_fake_measurements
+
 # Get a set of 10 recommendation
 rec = campaign.recommend(batch_size=10)
 
@@ -72,8 +74,8 @@ rec = campaign.recommend(batch_size=10)
 rec_finished = rec.iloc[:5]
 rec_pending = rec.iloc[5:]
 
-# Add target measurements to the finished part. Here we add a random number
-rec_finished["Target_max"] = 1337
+# Add target measurements to the finished part. Here we add fake results
+add_fake_measurements(rec_finished, campaign.targets)
 campaign.add_measurements(rec_finished)
 
 # Get the next set of recommendations, incorporating the still unfinished experiments.
diff --git a/docs/userguide/userguide.md b/docs/userguide/userguide.md
index 07e275be5..be252f3d5 100644
--- a/docs/userguide/userguide.md
+++ b/docs/userguide/userguide.md
@@ -15,4 +15,5 @@ Simulation <simulation>
 Surrogates <surrogates>
 Targets <targets>
 Transfer Learning <transfer_learning>
+Utilities <utils>
 ```
\ No newline at end of file
diff --git a/docs/userguide/utils.md b/docs/userguide/utils.md
new file mode 100644
index 000000000..3d421b0a3
--- /dev/null
+++ b/docs/userguide/utils.md
@@ -0,0 +1,135 @@
+# Utilities
+
+BayBE comes with a set of useful functions that can make your life easier in certain
+scenarios.
+
+## Search Space Memory Estimation
+In search spaces that have discrete parts, the memory needed to store the respective
+data can become excessively large as the number of points grows with the amount of
+possible combinations arising form all discrete parameter values.
+
+The [`SearchSpace.estimate_product_space_size`](baybe.searchspace.core.SearchSpace.estimate_product_space_size)
+and [`SubspaceDiscrete.estimate_product_space_size`](baybe.searchspace.discrete.SubspaceDiscrete.estimate_product_space_size)
+utilities allow estimating the memory needed to represent the discrete subspace. 
+They return a [`MemorySize`](baybe.searchspace.discrete.MemorySize) object that
+contains some relevant estimates:
+
+```python
+import numpy as np
+
+from baybe.parameters import NumericalDiscreteParameter
+from baybe.searchspace import SearchSpace
+
+# This creates 10 parameters with 20 values each.
+# The resulting space would have 20^10 entries, requiring around 745 TB of memory for
+# both experimental and computational representation of the search space.
+parameters = [
+    NumericalDiscreteParameter(name=f"p{k+1}", values=np.linspace(0, 100, 20))
+    for k in range(10)
+]
+
+# Estimate the required memory for such a space
+mem_estimate = SearchSpace.estimate_product_space_size(parameters)
+
+# Print quantities of interest
+print("Experimental Representation")
+print(f"Estimated size: {mem_estimate.exp_rep_human_readable}")
+print(f"Estimated size in Bytes: {mem_estimate.exp_rep_bytes}")
+print(f"Expected data frame shape: {mem_estimate.exp_rep_shape}")
+
+print("Computational Representation")
+print(f"Estimated size: {mem_estimate.comp_rep_human_readable}")
+print(f"Estimated size in Bytes: {mem_estimate.comp_rep_bytes}")
+print(f"Expected data frame shape: {mem_estimate.comp_rep_shape}")
+```
+
+```{admonition} Estimation with Constraints
+:class: warning
+{meth}`~baybe.searchspace.core.SearchSpace.estimate_product_space_size`
+currently does not include the influence of potential constraints in your search space
+as it is generally very hard to incorporate the effect of arbitrary constraints without
+actually building the entire space. Hence, you should always **treat the number you get
+as upper bound** of required memory. This can still be useful – for instance if your
+estimate already is several Exabytes, it is unlikely that most computers would be able
+to handle the result even if there are constraints present.
+```
+
+```{admonition} Memory During Optimization
+:class: warning
+{meth}`~baybe.searchspace.core.SearchSpace.estimate_product_space_size`
+only estimates the memory required to handle the search space. **It does not estimate
+the memory required during optimization**, which can be of a similar magnitude, but
+generally depends on additional factors.
+```
+
+```{admonition} Influence of Continuous Parameters
+:class: info
+Continuous parameters do not influence the size of the discrete search space part.
+Hence, they are ignored by the utility.
+```
+
+```{admonition} Efficient Search Space Creation
+:class: tip
+If you run into issues creating large search spaces, as for instance in mixture
+use cases, you should consider resorting to more specialized ways of creation by
+invoking alternative search space constructors like 
+{meth}`~baybe.searchspace.discrete.SubspaceDiscrete.from_dataframe`
+or 
+{meth}`~baybe.searchspace.discrete.SubspaceDiscrete.from_simplex`.
+Instead of creating a product space first and then filtering it down
+according to constraints, they offer a more direct and thus efficient path to the 
+desired result, typically requiring substantially less memory. 
+For example, {meth}`~baybe.searchspace.discrete.SubspaceDiscrete.from_simplex` 
+includes the mixture constraint already *during* the product creation. 
+In addition, BayBE can also be installed with its optional `polars` dependency 
+(`pip install baybe[polars]`) that activates efficient machinery for constraint handling.
+```
+
+## Reproducibility
+In some scenarios, for instance when testing your code setup, it can be useful to fix
+the random seeds for all relevant engines to generate reproducible results. BayBE offers
+the [`set_random_seed`](baybe.utils.random.set_random_seed) utility for this purpose:
+
+```python
+from baybe.utils.random import set_random_seed
+
+# Set the global random seed for all relevant engines
+set_random_seed(1337)
+
+# Assuming we have a prepared campaign
+campaign.recommend(5)
+```
+
+Setting the global random seed can be undesirable if there are other packages in your
+setup. For this, BayBE offers [`temporary_seed`](baybe.utils.random.temporary_seed):
+
+```python
+from baybe.utils.random import temporary_seed
+
+# Set the random seed for all relevant engines temporarily within the context
+with temporary_seed(1337):
+    campaign.recommend(5)
+```
+
+## Adding Fake Target Measurements and Parameter Noise
+When creating test scripts, it is often useful to try the recommendation loop for a few
+iterations. However, this requires some arbitrary target measurements to be set. Instead
+of coming up with a custom logic every time, you can use the
+[`add_fake_measurements`](baybe.utils.dataframe.add_fake_measurements) utility to add fake target
+measurements and the [`add_parameter_noise`](baybe.utils.dataframe.add_parameter_noise)
+utility to add artificial parameter noise:
+
+```python
+from baybe.utils.dataframe import add_fake_measurements, add_parameter_noise
+
+# Get recommendations
+recommendations = campaign.recommend(5)
+
+# Add fake target measurements and artificial parameter noise to the recommendations.
+# The utilities modify the dataframes inplace.
+measurements = recommendations.copy()
+add_fake_measurements(measurements, campaign.targets)
+add_parameter_noise(measurements, campaign.parameters)
+
+# Now continue the loop, e.g. by adding the measurements...
+```
diff --git a/examples/Basics/campaign.py b/examples/Basics/campaign.py
index 50e2b912c..a74f1fc8a 100644
--- a/examples/Basics/campaign.py
+++ b/examples/Basics/campaign.py
@@ -11,7 +11,7 @@
 from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Setup
 
@@ -82,10 +82,10 @@
 
 # Adding target values is done by creating a new column in the `recommendation`
 # dataframe named after the target.
-# In this example, we use the `add_fake_results()` utility to create fake results.
+# In this example, we use the `add_fake_measurements()` utility to create fake results.
 # We then update the campaign by adding the measurements.
 
-add_fake_results(recommendation, campaign.targets)
+add_fake_measurements(recommendation, campaign.targets)
 print("\n\nRecommended experiments with fake measured values: ")
 print(recommendation)
 
diff --git a/examples/Basics/recommenders.py b/examples/Basics/recommenders.py
index 4759041df..80ac03f41 100644
--- a/examples/Basics/recommenders.py
+++ b/examples/Basics/recommenders.py
@@ -29,7 +29,7 @@
 from baybe.surrogates.base import Surrogate
 from baybe.targets import NumericalTarget
 from baybe.utils.basic import get_subclasses
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Available recommenders suitable for initial recommendation
 
@@ -179,7 +179,7 @@
 print("\n\nRecommended experiments: ")
 print(recommendation)
 
-add_fake_results(recommendation, campaign.targets)
+add_fake_measurements(recommendation, campaign.targets)
 print("\n\nRecommended experiments with fake measured values: ")
 print(recommendation)
 
diff --git a/examples/Constraints_Discrete/custom_constraints.py b/examples/Constraints_Discrete/custom_constraints.py
index 866688336..4b1ce6f7e 100644
--- a/examples/Constraints_Discrete/custom_constraints.py
+++ b/examples/Constraints_Discrete/custom_constraints.py
@@ -23,7 +23,7 @@
 )
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup
 
@@ -156,5 +156,5 @@ def custom_function(df: pd.DataFrame) -> pd.Series:
     )
 
     rec = campaign.recommend(batch_size=5)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
diff --git a/examples/Constraints_Discrete/dependency_constraints.py b/examples/Constraints_Discrete/dependency_constraints.py
index 737bedc33..b61e9e090 100644
--- a/examples/Constraints_Discrete/dependency_constraints.py
+++ b/examples/Constraints_Discrete/dependency_constraints.py
@@ -23,7 +23,7 @@
 )
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup
 
@@ -113,5 +113,5 @@
     )
 
     rec = campaign.recommend(batch_size=5)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
diff --git a/examples/Constraints_Discrete/exclusion_constraints.py b/examples/Constraints_Discrete/exclusion_constraints.py
index eb81ef2fc..c776f660e 100644
--- a/examples/Constraints_Discrete/exclusion_constraints.py
+++ b/examples/Constraints_Discrete/exclusion_constraints.py
@@ -24,7 +24,7 @@
 )
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup
 
@@ -144,5 +144,5 @@
     )
 
     rec = campaign.recommend(batch_size=5)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
diff --git a/examples/Constraints_Discrete/mixture_constraints.py b/examples/Constraints_Discrete/mixture_constraints.py
index 7848043cc..3a07922a1 100644
--- a/examples/Constraints_Discrete/mixture_constraints.py
+++ b/examples/Constraints_Discrete/mixture_constraints.py
@@ -27,7 +27,7 @@
 from baybe.parameters import NumericalDiscreteParameter, SubstanceParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup
 
@@ -175,5 +175,5 @@
     )
 
     rec = campaign.recommend(batch_size=5)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
diff --git a/examples/Constraints_Discrete/prodsum_constraints.py b/examples/Constraints_Discrete/prodsum_constraints.py
index 2d547e61b..e1e85d72f 100644
--- a/examples/Constraints_Discrete/prodsum_constraints.py
+++ b/examples/Constraints_Discrete/prodsum_constraints.py
@@ -25,7 +25,7 @@
 )
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup
 
@@ -141,5 +141,5 @@
     )
 
     rec = campaign.recommend(batch_size=5)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
diff --git a/examples/Custom_Surrogates/custom_pretrained.py b/examples/Custom_Surrogates/custom_pretrained.py
index 5bdb89657..0e7ef2fcc 100644
--- a/examples/Custom_Surrogates/custom_pretrained.py
+++ b/examples/Custom_Surrogates/custom_pretrained.py
@@ -26,7 +26,7 @@
 from baybe.searchspace import SearchSpace
 from baybe.surrogates import CustomONNXSurrogate
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results, to_tensor
+from baybe.utils.dataframe import add_fake_measurements, to_tensor
 
 ### Experiment Setup
 
@@ -117,7 +117,7 @@
 
 # Add some fake results
 
-add_fake_results(recommendation, campaign.targets)
+add_fake_measurements(recommendation, campaign.targets)
 campaign.add_measurements(recommendation)
 
 ### Model Outputs
diff --git a/examples/Custom_Surrogates/surrogate_params.py b/examples/Custom_Surrogates/surrogate_params.py
index 88fe380ad..406371887 100644
--- a/examples/Custom_Surrogates/surrogate_params.py
+++ b/examples/Custom_Surrogates/surrogate_params.py
@@ -26,7 +26,7 @@
 from baybe.searchspace import SearchSpace
 from baybe.surrogates import NGBoostSurrogate
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment Setup
 
@@ -103,7 +103,7 @@
 print(recommendation)
 
 # Add some fake results
-add_fake_results(recommendation, campaign.targets)
+add_fake_measurements(recommendation, campaign.targets)
 campaign.add_measurements(recommendation)
 
 ### Model Outputs
diff --git a/examples/Multi_Target/desirability.py b/examples/Multi_Target/desirability.py
index d5b90c0d8..28eca2a40 100644
--- a/examples/Multi_Target/desirability.py
+++ b/examples/Multi_Target/desirability.py
@@ -15,7 +15,7 @@
 from baybe.parameters import CategoricalParameter, NumericalDiscreteParameter
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 ### Experiment setup and creating the searchspace
 
@@ -107,7 +107,7 @@
 
 for kIter in range(N_ITERATIONS):
     rec = campaign.recommend(batch_size=3)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
     desirability = campaign.objective.transform(campaign.measurements)
 
diff --git a/tests/conftest.py b/tests/conftest.py
index 75f15ada6..c82630cd4 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -77,7 +77,7 @@
 )
 from baybe.utils.basic import hilberts_factory
 from baybe.utils.boolean import strtobool
-from baybe.utils.dataframe import add_fake_results, add_parameter_noise
+from baybe.utils.dataframe import add_fake_measurements, add_parameter_noise
 from baybe.utils.random import temporary_seed
 
 # Hypothesis settings
@@ -913,7 +913,7 @@ def run_iterations(
             rec = campaign.recommend(batch_size=batch_size)
             # dont use parameter noise for these tests
 
-            add_fake_results(rec, campaign.targets)
+            add_fake_measurements(rec, campaign.targets)
             if add_noise and (k % 2):
                 add_parameter_noise(rec, campaign.parameters, noise_level=0.02)
 
diff --git a/tests/docs/test_docs.py b/tests/docs/test_docs.py
index e7286d118..74d86796d 100644
--- a/tests/docs/test_docs.py
+++ b/tests/docs/test_docs.py
@@ -29,7 +29,15 @@ def test_code_executability(file: Path, campaign):
     test will be available in the executed code too.
     """
     userguide_code = "\n".join(extract_code_blocks(file, include_tilde=False))
-    exec(userguide_code)
+
+    # Create a fixed namespace, which is provided to exec as both global and local
+    # name space. This ensures that all snippets are executed in their own fresh
+    # environment unaffected by other snippets. The space for globals and locals must
+    # be the same, as otherwise exec uses separate scopes for specific patterns within
+    # the snippet (e.g. list comprehensions) causing unknown name errors despite
+    # correct import.
+    namespace = {"__builtins__": __builtins__, "campaign": campaign}
+    exec(userguide_code, namespace, namespace)
 
 
 # TODO: Needs a refactoring (files codeblocks should be auto-detected)
diff --git a/tests/simulate_telemetry.py b/tests/simulate_telemetry.py
index ce26db3a8..495f043d9 100644
--- a/tests/simulate_telemetry.py
+++ b/tests/simulate_telemetry.py
@@ -21,7 +21,7 @@
     VARNAME_TELEMETRY_USERNAME,
     get_user_details,
 )
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 dict_solvent = {
     "DMAc": r"CC(N(C)C)=O",
@@ -85,7 +85,7 @@
 campaign = Campaign(**config)
 for k in range(randint(4, 6)):
     dat = campaign.recommend(randint(2, 3))
-    add_fake_results(dat, campaign.targets)
+    add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
 # Fake User1 - 5 iterations
@@ -94,7 +94,7 @@
 campaign = Campaign(**config)
 for k in range(randint(2, 3)):
     dat = campaign.recommend(randint(3, 4))
-    add_fake_results(dat, campaign.targets)
+    add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
 # Fake User1a - Adds recommenations before calling recommend
@@ -104,7 +104,7 @@
 campaign.add_measurements(dat)
 for k in range(randint(2, 3)):
     dat = campaign.recommend(randint(3, 4))
-    add_fake_results(dat, campaign.targets)
+    add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
 # Fake User2 - 2 iterations
@@ -113,7 +113,7 @@
 campaign = Campaign(**config)
 for k in range(2):
     dat = campaign.recommend(4)
-    add_fake_results(dat, campaign.targets)
+    add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
 # Fake User3 - no telemetry
@@ -123,7 +123,7 @@
 campaign = Campaign(**config)
 for k in range(randint(5, 7)):
     dat = campaign.recommend(randint(2, 3))
-    add_fake_results(dat, campaign.targets)
+    add_fake_measurements(dat, campaign.targets)
     campaign.add_measurements(dat)
 
 # Cleanup
diff --git a/tests/test_input_output.py b/tests/test_input_output.py
index c8a69192c..bda924b7c 100644
--- a/tests/test_input_output.py
+++ b/tests/test_input_output.py
@@ -8,7 +8,7 @@
 from baybe.recommenders import BotorchRecommender
 from baybe.searchspace import SearchSpace
 from baybe.targets import NumericalTarget
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 # List of tests that are expected to fail (still missing implementation etc)
 param_xfails = []
@@ -26,7 +26,7 @@ def test_bad_parameter_input_value(campaign, good_reference_values, bad_val, req
         pytest.xfail()
 
     rec = campaign.recommend(batch_size=3)
-    add_fake_results(
+    add_fake_measurements(
         rec,
         campaign.targets,
         good_reference_values=good_reference_values,
@@ -49,7 +49,7 @@ def test_bad_target_input_value(campaign, good_reference_values, bad_val, reques
         pytest.xfail()
 
     rec = campaign.recommend(batch_size=3)
-    add_fake_results(
+    add_fake_measurements(
         rec,
         campaign.targets,
         good_reference_values=good_reference_values,
diff --git a/tests/test_pending_experiments.py b/tests/test_pending_experiments.py
index dc20cbfa5..961dd8a39 100644
--- a/tests/test_pending_experiments.py
+++ b/tests/test_pending_experiments.py
@@ -18,7 +18,7 @@
     TwoPhaseMetaRecommender,
 )
 from baybe.utils.basic import get_subclasses
-from baybe.utils.dataframe import add_fake_results, add_parameter_noise
+from baybe.utils.dataframe import add_fake_measurements, add_parameter_noise
 from baybe.utils.random import temporary_seed
 
 _discrete_params = ["Categorical_1", "Switch_1", "Num_disc_1"]
@@ -117,7 +117,7 @@ def test_pending_points(campaign, batch_size):
 
     # Perform a fake first iteration
     rec = campaign.recommend(batch_size)
-    add_fake_results(rec, campaign.targets)
+    add_fake_measurements(rec, campaign.targets)
     campaign.add_measurements(rec)
 
     # Get recommendations and set them as pending experiments while getting another set
@@ -161,7 +161,7 @@ def test_invalid_acqf(searchspace, recommender, objective, batch_size, acqf):
 
     # Get recommendation and add a fake results
     rec1 = recommender.recommend(batch_size, searchspace, objective)
-    add_fake_results(rec1, objective.targets)
+    add_fake_measurements(rec1, objective.targets)
 
     # Create fake pending experiments
     rec2 = rec1.copy()
diff --git a/tests/test_surrogate.py b/tests/test_surrogate.py
index 5f6869bc7..463725443 100644
--- a/tests/test_surrogate.py
+++ b/tests/test_surrogate.py
@@ -4,7 +4,7 @@
 
 from baybe.recommenders.pure.nonpredictive.sampling import RandomRecommender
 from baybe.surrogates.gaussian_process.core import GaussianProcessSurrogate
-from baybe.utils.dataframe import add_fake_results
+from baybe.utils.dataframe import add_fake_measurements
 
 
 @patch.object(GaussianProcessSurrogate, "_fit")
@@ -12,7 +12,7 @@ def test_caching(patched, searchspace, objective):
     """A second fit call with the same context does not trigger retraining."""
     # Prepare the setting
     measurements = RandomRecommender().recommend(3, searchspace, objective)
-    add_fake_results(measurements, objective.targets)
+    add_fake_measurements(measurements, objective.targets)
     surrogate = GaussianProcessSurrogate()
 
     # First call