diff --git a/tutorials/roofit/rf102_dataimport.C b/tutorials/roofit/rf102_dataimport.C
index 1c1929c0a214d..bc24567fddb12 100644
--- a/tutorials/roofit/rf102_dataimport.C
+++ b/tutorials/roofit/rf102_dataimport.C
@@ -19,22 +19,25 @@
 #include "TTree.h"
 #include "TH1D.h"
 #include "TRandom.h"
-using namespace RooFit;
 
-TH1 *makeTH1();
-TTree *makeTTree();
+TH1 *makeTH1(TRandom &trnd);
+TTree *makeTTree(TRandom &trnd);
 
 void rf102_dataimport()
 {
+   using namespace RooFit;
+
    // ---------------------------------------------------
    // I m p o r t i n g   R O O T   h i s t o g r a m s
    // ===================================================
 
+   TRandom3 trnd{};
+
    // I m p o r t   T H 1   i n t o   a   R o o D a t a H i s t
    // ---------------------------------------------------------
 
    // Create a ROOT TH1 histogram
-   TH1 *hh = makeTH1();
+   TH1 *hh = makeTH1(trnd);
 
    // Declare observable x
    RooRealVar x("x", "x", -10, 10);
@@ -80,7 +83,7 @@ void rf102_dataimport()
    // I m p o r t   T T r e e   i n t o   a   R o o D a t a S e t
    // -----------------------------------------------------------
 
-   TTree *tree = makeTTree();
+   TTree *tree = makeTTree(trnd);
 
    // Define 2nd observable y
    RooRealVar y("y", "y", -10, 10);
@@ -166,17 +169,17 @@ void rf102_dataimport()
 }
 
 // Create ROOT TH1 filled with a Gaussian distribution
-TH1 *makeTH1()
+TH1 *makeTH1(TRandom &trnd)
 {
    TH1D *hh = new TH1D("hh", "hh", 25, -10, 10);
    for (int i = 0; i < 100; i++) {
-      hh->Fill(gRandom->Gaus(0, 3));
+      hh->Fill(trnd.Gaus(0, 3));
    }
    return hh;
 }
 
 // Create ROOT TTree filled with a Gaussian distribution in x and a uniform distribution in y
-TTree *makeTTree()
+TTree *makeTTree(TRandom &trnd)
 {
    TTree *tree = new TTree("tree", "tree");
    double *px = new double;
@@ -184,8 +187,8 @@ TTree *makeTTree()
    tree->Branch("x", px, "x/D");
    tree->Branch("y", py, "y/D");
    for (int i = 0; i < 100; i++) {
-      *px = gRandom->Gaus(0, 3);
-      *py = gRandom->Uniform() * 30 - 15;
+      *px = trnd.Gaus(0, 3);
+      *py = trnd.Uniform() * 30 - 15;
       tree->Fill();
    }
    return tree;
diff --git a/tutorials/roofit/rf102_dataimport.py b/tutorials/roofit/rf102_dataimport.py
index 8670d535f3295..30cf8591a0a53 100644
--- a/tutorials/roofit/rf102_dataimport.py
+++ b/tutorials/roofit/rf102_dataimport.py
@@ -15,17 +15,17 @@
 from array import array
 
 
-def makeTH1():
+def makeTH1(trnd):
 
     # Create ROOT ROOT.TH1 filled with a Gaussian distribution
 
     hh = ROOT.TH1D("hh", "hh", 25, -10, 10)
     for i in range(100):
-        hh.Fill(ROOT.gRandom.Gaus(0, 3))
+        hh.Fill(trnd.Gaus(0, 3))
     return hh
 
 
-def makeTTree():
+def makeTTree(trnd):
     # Create ROOT ROOT.TTree filled with a Gaussian distribution in x and a
     # uniform distribution in y
 
@@ -35,11 +35,12 @@ def makeTTree():
     tree.Branch("x", px, "x/D")
     tree.Branch("y", py, "y/D")
     for i in range(100):
-        px[0] = ROOT.gRandom.Gaus(0, 3)
-        py[0] = ROOT.gRandom.Uniform() * 30 - 15
+        px[0] = trnd.Gaus(0, 3)
+        py[0] = trnd.Uniform() * 30 - 15
         tree.Fill()
     return tree
 
+trnd = ROOT.TRandom3()
 
 ############################
 # Importing ROOT histograms
@@ -47,7 +48,7 @@ def makeTTree():
 # Import ROOT TH1 into a RooDataHist
 # ---------------------------------------------------------
 # Create a ROOT TH1 histogram
-hh = makeTH1()
+hh = makeTH1(trnd)
 
 # Declare observable x
 x = ROOT.RooRealVar("x", "x", -10, 10)
@@ -90,7 +91,7 @@ def makeTTree():
 # -----------------------------------------------------------
 # Import ROOT TTree into a RooDataSet
 
-tree = makeTTree()
+tree = makeTTree(trnd)
 
 # Define 2nd observable y
 y = ROOT.RooRealVar("y", "y", -10, 10)
diff --git a/tutorials/roofit/rf303_conditional.C b/tutorials/roofit/rf303_conditional.C
index 5effeacb03a9a..e6d3186413fb9 100644
--- a/tutorials/roofit/rf303_conditional.C
+++ b/tutorials/roofit/rf303_conditional.C
@@ -91,6 +91,8 @@ void rf303_conditional()
 
 RooDataSet *makeFakeDataXY()
 {
+   TRandom3 trnd{};
+
    RooRealVar x("x", "x", -10, 10);
    RooRealVar y("y", "y", -10, 10);
    RooArgSet coord(x, y);
@@ -98,8 +100,8 @@ RooDataSet *makeFakeDataXY()
    RooDataSet *d = new RooDataSet("d", "d", RooArgSet(x, y));
 
    for (int i = 0; i < 10000; i++) {
-      double tmpy = gRandom->Gaus(0, 10);
-      double tmpx = gRandom->Gaus(0.5 * tmpy, 1);
+      double tmpy = trnd.Gaus(0, 10);
+      double tmpx = trnd.Gaus(0.5 * tmpy, 1);
       if (fabs(tmpy) < 10 && fabs(tmpx) < 10) {
          x.setVal(tmpx);
          y.setVal(tmpy);
diff --git a/tutorials/roofit/rf303_conditional.py b/tutorials/roofit/rf303_conditional.py
index 69bf1fd1a9392..e3b4c2f93d9e2 100644
--- a/tutorials/roofit/rf303_conditional.py
+++ b/tutorials/roofit/rf303_conditional.py
@@ -17,6 +17,9 @@
 
 
 def makeFakeDataXY():
+
+    trnd = ROOT.TRandom3()
+
     x = ROOT.RooRealVar("x", "x", -10, 10)
     y = ROOT.RooRealVar("y", "y", -10, 10)
     coord = {x, y}
@@ -24,8 +27,8 @@ def makeFakeDataXY():
     d = ROOT.RooDataSet("d", "d", coord)
 
     for i in range(10000):
-        tmpy = ROOT.gRandom.Gaus(0, 10)
-        tmpx = ROOT.gRandom.Gaus(0.5 * tmpy, 1)
+        tmpy = trnd.Gaus(0, 10)
+        tmpx = trnd.Gaus(0.5 * tmpy, 1)
         if (abs(tmpy) < 10) and (abs(tmpx) < 10):
             x.setVal(tmpx)
             y.setVal(tmpy)
diff --git a/tutorials/roofit/rf401_importttreethx.C b/tutorials/roofit/rf401_importttreethx.C
index 826d334caf789..0fc3eebde4b50 100644
--- a/tutorials/roofit/rf401_importttreethx.C
+++ b/tutorials/roofit/rf401_importttreethx.C
@@ -26,18 +26,20 @@
 
 using namespace RooFit;
 
-TH1 *makeTH1(const char *name, double mean, double sigma);
-TTree *makeTTree();
+TH1 *makeTH1(TRandom &trnd, const char *name, double mean, double sigma);
+TTree *makeTTree(TRandom &trnd);
 
 void rf401_importttreethx()
 {
+   TRandom3 trnd{};
+
    // I m p o r t  m u l t i p l e   T H 1   i n t o   a   R o o D a t a H i s t
    // --------------------------------------------------------------------------
 
    // Create thee ROOT TH1 histograms
-   TH1 *hh_1 = makeTH1("hh1", 0, 3);
-   TH1 *hh_2 = makeTH1("hh2", -3, 1);
-   TH1 *hh_3 = makeTH1("hh3", +3, 4);
+   TH1 *hh_1 = makeTH1(trnd, "hh1", 0, 3);
+   TH1 *hh_2 = makeTH1(trnd, "hh2", -3, 1);
+   TH1 *hh_3 = makeTH1(trnd, "hh3", +3, 4);
 
    // Declare observable x
    RooRealVar x("x", "x", -10, 10);
@@ -61,7 +63,7 @@ void rf401_importttreethx()
    // I m p o r t i n g   a   T T r e e   i n t o   a   R o o D a t a S e t   w i t h   c u t s
    // -----------------------------------------------------------------------------------------
 
-   TTree *tree = makeTTree();
+   TTree *tree = makeTTree(trnd);
 
    // Define observables y,z
    RooRealVar y("y", "y", -10, 10);
@@ -108,18 +110,18 @@ void rf401_importttreethx()
    dsABC.Print();
 }
 
-TH1 *makeTH1(const char *name, double mean, double sigma)
+TH1 *makeTH1(TRandom &trnd, const char *name, double mean, double sigma)
 {
    // Create ROOT TH1 filled with a Gaussian distribution
 
    TH1D *hh = new TH1D(name, name, 100, -10, 10);
    for (int i = 0; i < 1000; i++) {
-      hh->Fill(gRandom->Gaus(mean, sigma));
+      hh->Fill(trnd.Gaus(mean, sigma));
    }
    return hh;
 }
 
-TTree *makeTTree()
+TTree *makeTTree(TRandom &trnd)
 {
    // Create ROOT TTree filled with a Gaussian distribution in x and a uniform distribution in y
 
@@ -133,9 +135,9 @@ TTree *makeTTree()
    tree->Branch("z", pz, "z/D");
    tree->Branch("i", pi, "i/I");
    for (int i = 0; i < 100; i++) {
-      *px = gRandom->Gaus(0, 3);
-      *py = gRandom->Uniform() * 30 - 15;
-      *pz = gRandom->Gaus(0, 5);
+      *px = trnd.Gaus(0, 3);
+      *py = trnd.Uniform() * 30 - 15;
+      *pz = trnd.Gaus(0, 5);
       *pi = i % 3;
       tree->Fill();
    }
diff --git a/tutorials/roofit/rf401_importttreethx.py b/tutorials/roofit/rf401_importttreethx.py
index dccdc53c27ea2..0c9bfdb3d3deb 100644
--- a/tutorials/roofit/rf401_importttreethx.py
+++ b/tutorials/roofit/rf401_importttreethx.py
@@ -16,17 +16,17 @@
 from array import array
 
 
-def makeTH1(name, mean, sigma):
+def makeTH1(trnd, name, mean, sigma):
     """Create ROOT TH1 filled with a Gaussian distribution."""
 
     hh = ROOT.TH1D(name, name, 100, -10, 10)
     for i in range(1000):
-        hh.Fill(ROOT.gRandom.Gaus(mean, sigma))
+        hh.Fill(trnd.Gaus(mean, sigma))
 
     return hh
 
 
-def makeTTree():
+def makeTTree(trnd):
     """Create ROOT ROOT.TTree filled with a Gaussian distribution in x and a uniform distribution in y."""
 
     tree = ROOT.TTree("tree", "tree")
@@ -39,22 +39,23 @@ def makeTTree():
     tree.Branch("z", pz, "z/D")
     tree.Branch("i", pi, "i/I")
     for i in range(100):
-        px[0] = ROOT.gRandom.Gaus(0, 3)
-        py[0] = ROOT.gRandom.Uniform() * 30 - 15
-        pz[0] = ROOT.gRandom.Gaus(0, 5)
+        px[0] = trnd.Gaus(0, 3)
+        py[0] = trnd.Uniform() * 30 - 15
+        pz[0] = trnd.Gaus(0, 5)
         pi[0] = i % 3
         tree.Fill()
 
     return tree
 
+trnd = ROOT.TRandom3()
 
 # Import multiple TH1 into a RooDataHist
 # ----------------------------------------------------------
 
 # Create thee ROOT ROOT.TH1 histograms
-hh_1 = makeTH1("hh1", 0, 3)
-hh_2 = makeTH1("hh2", -3, 1)
-hh_3 = makeTH1("hh3", +3, 4)
+hh_1 = makeTH1(trnd, "hh1", 0, 3)
+hh_2 = makeTH1(trnd, "hh2", -3, 1)
+hh_3 = makeTH1(trnd, "hh3", +3, 4)
 
 # Declare observable x
 x = ROOT.RooRealVar("x", "x", -10, 10)
@@ -76,7 +77,7 @@ def makeTTree():
 # Importing a ROOT TTree into a RooDataSet with cuts
 # --------------------------------------------------------------------
 
-tree = makeTTree()
+tree = makeTTree(trnd)
 
 # Define observables y,z
 y = ROOT.RooRealVar("y", "y", -10, 10)
diff --git a/tutorials/roofit/rf609_xychi2fit.C b/tutorials/roofit/rf609_xychi2fit.C
index 52c6bbf971a8d..baf39741e81ed 100644
--- a/tutorials/roofit/rf609_xychi2fit.C
+++ b/tutorials/roofit/rf609_xychi2fit.C
@@ -37,7 +37,10 @@ void rf609_xychi2fit()
    RooRealVar y("y", "y", -10, 200);
    RooDataSet dxy("dxy", "dxy", {x, y}, StoreError({x, y}));
 
+   TRandom3 trnd{};
+
    // Fill an example dataset with X,err(X),Y,err(Y) values
+   RooArgSet vars{x, y};
    for (int i = 0; i <= 10; i++) {
 
       // Set X value and error
@@ -45,10 +48,10 @@ void rf609_xychi2fit()
       x.setError(i < 5 ? 0.5 / 1. : 1.0 / 1.);
 
       // Set Y value and error
-      y = x.getVal() * x.getVal() + 4 * fabs(gRandom->Gaus());
+      y = x.getVal() * x.getVal() + 4 * std::abs(trnd.Gaus());
       y.setError(sqrt(y.getVal()));
 
-      dxy.add({x, y});
+      dxy.add(vars);
    }
 
    // P e r f o r m   c h i 2   f i t   t o   X + / - d x   a n d   Y + / - d Y   v a l u e s
diff --git a/tutorials/roofit/rf609_xychi2fit.py b/tutorials/roofit/rf609_xychi2fit.py
index 954d3a181bda9..201d3c33dcf38 100644
--- a/tutorials/roofit/rf609_xychi2fit.py
+++ b/tutorials/roofit/rf609_xychi2fit.py
@@ -14,6 +14,7 @@
 import ROOT
 import math
 
+trnd = ROOT.TRandom3()
 
 # Create dataset with X and Y values
 # -------------------------------------------------------------------
@@ -35,7 +36,7 @@
     x.setError((0.5 / 1.0) if (i < 5) else (1.0 / 1.0))
 
     # Set Y value and error
-    y.setVal(x.getVal() * x.getVal() + 4 * abs(ROOT.gRandom.Gaus()))
+    y.setVal(x.getVal() * x.getVal() + 4 * abs(trnd.Gaus()))
     y.setError(math.sqrt(y.getVal()))
 
     dxy.add({x, y})