diff --git a/CHANGELOG.md b/CHANGELOG.md
index a876485..fb6687e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -24,6 +24,7 @@ All notable changes to this project will be documented in this file.
 
 ### Changed
 
+- Removed the redundant function `evaluate_sample` from `sampling.py`
 
 ### Fixed
 
diff --git a/eulerpi/core/sampling.py b/eulerpi/core/sampling.py
index be8a982..0a2fda0 100644
--- a/eulerpi/core/sampling.py
+++ b/eulerpi/core/sampling.py
@@ -27,42 +27,6 @@
 from eulerpi.core.result_manager import ResultManager
 from eulerpi.core.transformations import eval_log_transformed_density
 
-# TODO: This works on the blob
-# Return the samples.
-# return sampler.get_chain(discard=0, thin=1, flat=True)
-# TODO: This stores the sample as 2d array in the format walker1_step1, walker2_step1, walker3_step1, walker1_step2, walker2_step2, walker3_step2, ...
-# sampler_results = samplerBlob.reshape(
-#     num_walkers * num_steps, sampling_dim + data_dim + 1
-# )
-
-
-def evaluate_sample(
-    param: np.ndarray,
-    model: Model,
-    data: np.ndarray,
-    data_transformation: DataTransformation,
-    data_stdevs: np.ndarray,
-    slice: np.ndarray,
-) -> typing.Tuple[float, np.ndarray]:
-    """Evaluate the log transformed density at the given parameter values.
-
-    Args:
-        param (np.ndarray): parameter values
-        model (Model): The model which will be sampled
-        data (np.ndarray): data
-        data_transformation (DataTransformation): The data transformation used to normalize the data.
-        data_stdevs (np.ndarray): kernel width for the data
-        slice (np.ndarray): slice of the parameter space which will be sampled
-
-    Returns:
-        typing.Tuple[float, np.ndarray]: log transformed density and the sampler result
-    """
-
-    log_samplerresult = eval_log_transformed_density(
-        param, model, data, data_transformation, data_stdevs, slice
-    )
-    return log_samplerresult
-
 
 def run_emcee_once(
     model: Model,
@@ -102,7 +66,7 @@ def run_emcee_once(
         sampler = emcee.EnsembleSampler(
             num_walkers,
             sampling_dim,
-            evaluate_sample,
+            eval_log_transformed_density,
             pool=pool,
             args=[model, data, data_transformation, data_stdevs, slice],
         )