diff --git a/pypesto/objective/aesara.py b/pypesto/objective/aesara.py
index e37210239..b9e654d6b 100644
--- a/pypesto/objective/aesara.py
+++ b/pypesto/objective/aesara.py
@@ -3,7 +3,7 @@
 
 Adds an interface for the construction of loss functions
 incorporating aesara models. This permits computation of derivatives using a
-combination of objective based methods and aeara based backpropagation.
+combination of objective based methods and aesara based backpropagation.
 """
 
 import copy
@@ -43,7 +43,7 @@ class AesaraObjective(ObjectiveBase):
     objective:
         The `pypesto.ObjectiveBase` to wrap.
     aet_x:
-        Tensor variables that that define the variables of `aet_fun`
+        Tensor variables that define the variables of `aet_fun`
     aet_fun:
         Aesara function that maps `aet_x` to the variables of `objective`
     coeff:
@@ -228,7 +228,7 @@ class AesaraObjectiveOp(Op):
     Parameters
     ----------
     obj:
-        Base aseara objective
+        Base aesara objective
     coeff:
         Multiplicative coefficient for the objective function value
     """
@@ -248,7 +248,7 @@ def __init__(self, obj: AesaraObjective, coeff: Optional[float] = 1.0):
 
     def perform(self, node, inputs, outputs, params=None):  # noqa
         # note that we use precomputed values from the outer
-        # AesaraObjective.call_unprocessed here, which which means we can
+        # AesaraObjective.call_unprocessed here, which means we can
         # ignore inputs here
         log_prob = self._coeff * self._objective.inner_ret[FVAL]
         outputs[0][0] = np.array(log_prob)
@@ -275,7 +275,7 @@ class AesaraObjectiveGradOp(Op):
     Parameters
     ----------
     obj:
-        Base aseara objective
+        Base aesara objective
     coeff:
         Multiplicative coefficient for the objective function value
     """
@@ -321,7 +321,7 @@ class AesaraObjectiveHessOp(Op):
     Parameters
     ----------
     obj:
-        Base aseara objective
+        Base aesara objective
     coeff:
         Multiplicative coefficient for the objective function value
     """
@@ -335,7 +335,7 @@ def __init__(self, obj: AesaraObjective, coeff: Optional[float] = 1.0):
 
     def perform(self, node, inputs, outputs, params=None):  # noqa
         # note that we use precomputed values from the outer
-        # AesaraObjective.call_unprocessed here, which which means we can
+        # AesaraObjective.call_unprocessed here, which means we can
         # ignore inputs here
         log_prob_hess = self._coeff * self._objective.inner_ret[HESS]
         outputs[0][0] = log_prob_hess
diff --git a/pypesto/objective/aggregated.py b/pypesto/objective/aggregated.py
index febece237..9ce6c084f 100644
--- a/pypesto/objective/aggregated.py
+++ b/pypesto/objective/aggregated.py
@@ -111,7 +111,7 @@ def get_config(self) -> dict:
 
 def aggregate_results(rvals: Sequence[ResultDict]) -> ResultDict:
     """
-    Aggregrate the results from the provided ResultDicts into a single one.
+    Aggregate the results from the provided ResultDicts into a single one.
 
     Parameters
     ----------
diff --git a/pypesto/objective/base.py b/pypesto/objective/base.py
index c4a95529f..0b120eef8 100644
--- a/pypesto/objective/base.py
+++ b/pypesto/objective/base.py
@@ -255,7 +255,7 @@ def get_config(self) -> dict:
         """
         Get the configuration information of the objective function.
 
-        Return it as a dictonary.
+        Return it as a dictionary.
         """
         info = {'type': self.__class__.__name__}
         return info
diff --git a/pypesto/objective/finite_difference.py b/pypesto/objective/finite_difference.py
index 81f90d5b4..bb8f5c1a8 100644
--- a/pypesto/objective/finite_difference.py
+++ b/pypesto/objective/finite_difference.py
@@ -181,7 +181,7 @@ def _update(
         # shape (n_delta, n_par, ...)
         nablas = np.array(nablas)
 
-        # The stability vector is the the absolute difference of Jacobian
+        # The stability vector is the absolute difference of Jacobian
         #  entries towards smaller and larger deltas, thus indicating the
         #  change in the approximation when changing delta.
         # This is done separately for each parameter. Then, for each the delta