diff --git a/examples/sampler/initial-sampling-method-integer.py b/examples/sampler/initial-sampling-method-integer.py
index c66a9508b..6d775a087 100644
--- a/examples/sampler/initial-sampling-method-integer.py
+++ b/examples/sampler/initial-sampling-method-integer.py
@@ -13,7 +13,7 @@
 (10 by default). The downside to this is that there is no guarantee that
 these samples are spread out evenly across all the dimensions.
 
-Sampling methods as Latin hypercube, Sobol, Halton and Hammersly
+Sampling methods as Latin hypercube, Sobol', Halton and Hammersly
 take advantage of the fact that we know beforehand how many random
 points we want to sample. Then these points can be "spread out" in
 such a way that each dimension is explored.
@@ -64,15 +64,15 @@ def plot_searchspace(x, title):
 x_label.append("random")
 
 #############################################################################
-# Sobol
-# -----
+# Sobol'
+# ------
 
 sobol = Sobol()
 x = sobol.generate(space.dimensions, n_samples)
-plot_searchspace(x, 'Sobol')
+plot_searchspace(x, "Sobol'")
 print("empty fields: %d" % (36 - np.size(np.unique(x, axis=0), 0)))
 pdist_data.append(pdist(x).flatten())
-x_label.append("sobol")
+x_label.append("sobol'")
 
 #############################################################################
 # Classic latin hypercube sampling
diff --git a/examples/sampler/initial-sampling-method.py b/examples/sampler/initial-sampling-method.py
index ad0e7f903..ba734742f 100644
--- a/examples/sampler/initial-sampling-method.py
+++ b/examples/sampler/initial-sampling-method.py
@@ -14,7 +14,7 @@
 (10 by default). The downside to this is that there is no guarantee that
 these samples are spread out evenly across all the dimensions.
 
-Sampling methods as Latin hypercube, Sobol, Halton and Hammersly
+Sampling methods as Latin hypercube, Sobol', Halton and Hammersly
 take advantage of the fact that we know beforehand how many random
 points we want to sample. Then these points can be "spread out" in
 such a way that each dimension is explored.
@@ -64,14 +64,14 @@ def plot_searchspace(x, title):
 x_label.append("random")
 
 #############################################################################
-# Sobol
-# -----
+# Sobol'
+# ------
 
 sobol = Sobol()
 x = sobol.generate(space.dimensions, n_samples)
-plot_searchspace(x, 'Sobol')
+plot_searchspace(x, "Sobol'")
 pdist_data.append(pdist(x).flatten())
-x_label.append("sobol")
+x_label.append("sobol'")
 
 #############################################################################
 # Classic Latin hypercube sampling
diff --git a/examples/sampler/sampling_comparison.py b/examples/sampler/sampling_comparison.py
index 819f5e06d..a52d92c0f 100644
--- a/examples/sampler/sampling_comparison.py
+++ b/examples/sampler/sampling_comparison.py
@@ -14,7 +14,7 @@
 
 * Halton sequence,
 * Hammersly sequence,
-* Sobol sequence and
+* Sobol' sequence and
 * Latin hypercube sampling
 
 as initial points. The purely random point generation is used as
@@ -153,7 +153,7 @@ def run_measure(initial_point_generator, n_initial_points=10):
 plot = plot_convergence([("random", dummy_res),
                         ("lhs", lhs_res),
                         ("lhs_maximin", lhs2_res),
-                        ("sobol", sobol_res),
+                        ("sobol'", sobol_res),
                         ("halton", halton_res),
                         ("hammersly", hammersly_res),
                         ("grid", grid_res)],
diff --git a/skopt/optimizer/base.py b/skopt/optimizer/base.py
index 8cde60774..ac26bd66e 100644
--- a/skopt/optimizer/base.py
+++ b/skopt/optimizer/base.py
@@ -84,7 +84,7 @@ def base_minimize(func, dimensions, base_estimator,
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,
diff --git a/skopt/optimizer/dummy.py b/skopt/optimizer/dummy.py
index 71902ee97..c372c243b 100644
--- a/skopt/optimizer/dummy.py
+++ b/skopt/optimizer/dummy.py
@@ -40,7 +40,7 @@ def dummy_minimize(func, dimensions, n_calls=100,
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,
diff --git a/skopt/optimizer/forest.py b/skopt/optimizer/forest.py
index 9ceada655..455a04cf0 100644
--- a/skopt/optimizer/forest.py
+++ b/skopt/optimizer/forest.py
@@ -92,7 +92,7 @@ def forest_minimize(func, dimensions, base_estimator="ET", n_calls=100,
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,
diff --git a/skopt/optimizer/gbrt.py b/skopt/optimizer/gbrt.py
index bd11aa854..de43d6fcc 100644
--- a/skopt/optimizer/gbrt.py
+++ b/skopt/optimizer/gbrt.py
@@ -79,7 +79,7 @@ def gbrt_minimize(func, dimensions, base_estimator=None,
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,
diff --git a/skopt/optimizer/gp.py b/skopt/optimizer/gp.py
index 83aebb731..67935a8e8 100644
--- a/skopt/optimizer/gp.py
+++ b/skopt/optimizer/gp.py
@@ -100,7 +100,7 @@ def gp_minimize(func, dimensions, base_estimator=None,
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,
diff --git a/skopt/optimizer/optimizer.py b/skopt/optimizer/optimizer.py
index a2c11e322..8aa32169d 100644
--- a/skopt/optimizer/optimizer.py
+++ b/skopt/optimizer/optimizer.py
@@ -76,7 +76,7 @@ class Optimizer(object):
         Sets a initial points generator. Can be either
 
         - `"random"` for uniform random numbers,
-        - `"sobol"` for a Sobol sequence,
+        - `"sobol"` for a Sobol' sequence,
         - `"halton"` for a Halton sequence,
         - `"hammersly"` for a Hammersly sequence,
         - `"lhs"` for a latin hypercube sequence,