diff --git a/test/test_01_loading.py b/test/test_01_loading.py
index 3ecb6b8..8116c4f 100644
--- a/test/test_01_loading.py
+++ b/test/test_01_loading.py
@@ -1,6 +1,12 @@
+import pytest
+
 def test_01_loading_test1_csv():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+    assert dp.numberLineups == 890
+    assert dp.numberTALineups == 443
+    assert dp.numberTPLineups == 447
 
 def test_01_loading_test1_csv_check_data():
     import pyWitness
diff --git a/test/test_02_processing.py b/test/test_02_processing.py
index c36a60c..b0d1e3e 100644
--- a/test/test_02_processing.py
+++ b/test/test_02_processing.py
@@ -1,3 +1,5 @@
+import pytest
+
 def test_02_processing_test1_csv():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
@@ -15,6 +17,12 @@ def test_02_processing_test1_csv_descriptive_stats():
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
     dp = dr.process()
     dp.printDescriptiveStats()
+    assert dp.dPrime == pytest.approx(1.9752208100241062, rel=1e-5)
+    assert dp.pAUC == pytest.approx(0.020542013220820013, rel=1e-5)
+    # overall correct ID rate
+    assert dp.data_rates.loc[("targetPresent", "suspectId")].max() == pytest.approx(0.6196868008948546, rel=1e-5)
+    # overall false ID rate
+    assert dp.data_rates.loc[("targetAbsent", "suspectId")].max() == pytest.approx(0.0474040632054176, rel=1e-5)
 
 def test_02_processing_test1_csv_response_time():
     import pyWitness
diff --git a/test/test_03_plotting.py b/test/test_03_plotting.py
index 0213bab..e49d84c 100644
--- a/test/test_03_plotting.py
+++ b/test/test_03_plotting.py
@@ -1,3 +1,5 @@
+import pytest
+
 def test_03_plotting_test1_csv_plot_roc():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
diff --git a/test/test_04_collapsing.py b/test/test_04_collapsing.py
index a9aa60d..c9660d2 100644
--- a/test/test_04_collapsing.py
+++ b/test/test_04_collapsing.py
@@ -1,3 +1,5 @@
+import pytest
+
 def test_04_collapsing_categorical_data():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
@@ -8,6 +10,8 @@ def test_04_collapsing_categorical_data():
     dp = dr.process()
     dp.plotCAC()
 
+    assert dp.numberLineups == 890
+
 def test_04_collapsing_continuous_data():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
diff --git a/test/test_05_fitting.py b/test/test_05_fitting.py
index f071a3c..16fffbb 100644
--- a/test/test_05_fitting.py
+++ b/test/test_05_fitting.py
@@ -1,3 +1,5 @@
+import pytest
+
 def test_05_fitting_test1_csv_indep_obs_eqvar():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
@@ -7,34 +9,271 @@ def test_05_fitting_test1_csv_indep_obs_eqvar():
     mf.setEqualVariance()
     mf.fit()
 
-def test_05_fitting_test1_csv_indep_obs_uneqvar_print_parameters():
+    assert mf.chi2 == pytest.approx(10.300411274463412, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 4
+    assert mf.chi2PerNDF == pytest.approx(2.575102818615853, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.03566019782522267, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.7976601843420954, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(0.6046983921244553, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.6046983921244553, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.4017022884785224, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.93548009449426, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.677475327674742, rel=1e-5)
+
+def test_05_fitting_test1_csv_indep_obs_uneqvar():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
     dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
     dp = dr.process()
     mf = pyWitness.ModelFitIndependentObservation(dp)
-    mf.printParameters()
+    mf.setUnequalVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(4.534824840657988, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 3
+    assert mf.chi2PerNDF == pytest.approx(1.5116082802193294, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.20920494600886963, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.8894645402811472, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.7886365473806328, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(0.4480311026689192, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.4480311026689192, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.5351065745707397, rel=1e-5)
+    assert mf.c2.value == pytest.approx(2.002341182989407, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.6308524212497644, rel=1e-5)
+
+def test_05_fitting_test1_csv_indep_obs_free():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 40, 60, 80, 100], labels=[1, 2, 3, 4])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(10.76520159182259, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 2
+    assert mf.chi2PerNDF == pytest.approx(5.382600795911295, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.004595853496030644, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0006818129624015098, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(0.7613182180813651, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.493581552198572, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.5701995798268649, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(-0.0012213835415482996, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.0012586054967293737, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.25713233870699, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.3660364365503272, rel=1e-5)
+    assert mf.c3.value == pytest.approx(1.5870619583040122, rel=1e-5)
+    assert mf.c4.value == pytest.approx(2.033939924498857, rel=1e-5)
+
+def test_05_fitting_test1_csv_best_rest_eqvar():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf = pyWitness.ModelFitBestRest(dp)
+    mf.setEqualVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(23.203557131914106, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 4
+    assert mf.chi2PerNDF == pytest.approx(5.800889282978527, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.00011530375016544081, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.03566192879208, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(-0.027650790308206018, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(-0.027650790308206018, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.7353510716101122, rel=1e-5)
+    assert mf.c2.value == pytest.approx(2.212736570519106, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.9491721140631633, rel=1e-5)
+
+def test_05_fitting_test1_csv_best_rest_uneqvar():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf = pyWitness.ModelFitBestRest(dp)
+    mf.setUnequalVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(7.868684226378687, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 3
+    assert mf.chi2PerNDF == pytest.approx(2.622894742126229, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.04880501500208234, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.038036936738798, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.6139740611429738, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(0.03895753863353904, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.03895753863353904, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.7546932765997934, rel=1e-5)
+    assert mf.c2.value == pytest.approx(2.168399339678346, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.7606531061053246, rel=1e-5)
+
+def test_05_fitting_test1_csv_best_rest_free():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 40, 60, 80, 100], labels=[1, 2, 3, 4])
+    dp = dr.process()
+    mf = pyWitness.ModelFitBestRest(dp)
+    mf.fit()
 
-def test_05_fitting_test1_csv_best_rest():
+    assert mf.chi2 == pytest.approx(9.92916150379401, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 2
+    assert mf.chi2PerNDF == pytest.approx(4.964580751897005, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.006980876815037118, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0018987441970468675, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(0.6861322853955167, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.4012317220792951, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.4268722771827042, rel=1e-5)
+    #assert mf.lureBetweenSigma.value == pytest.approx(0.00035201600917691706, abs=0.001)
+    #assert mf.targetBetweenSigma.value == pytest.approx(0.0008327066039283845, abs=0.001)
+    assert mf.c1.value == pytest.approx(1.2028664376190874, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.29788822134375, rel=1e-5)
+    assert mf.c3.value == pytest.approx(1.4905841431052758, rel=1e-5)
+
+def test_05_fitting_test1_csv_ensemble_eqvar():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
     dr.collapseContinuousData(column="confidence")
     dp = dr.process()
-    mf_br = pyWitness.ModelFitBestRest(dp)
+    mf = pyWitness.ModelFitEnsemble(dp)
+    mf.setEqualVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(23.20355799888823, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 4
+    assert mf.chi2PerNDF == pytest.approx(5.800889499722057, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.00011530370414902791, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.03564527876075, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(0.0025862757713492144, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.0025862757713492144, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.4461421641934356, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.8439293779396513, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.457635059181422, rel=1e-5)
+
+def test_05_fitting_test1_csv_ensemble_uneqvar():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf = pyWitness.ModelFitEnsemble(dp)
+    mf.setUnequalVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(7.868684371812639, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 3
+    assert mf.chi2PerNDF == pytest.approx(2.6228947906042133, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.04880501181888286, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.0380474980579812, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.6139499862139856, rel=1e-3)
+    #assert mf.lureBetweenSigma.value == pytest.approx(-0.045359525568252, rel=1e-5)
+    #assert mf.targetBetweenSigma.value == pytest.approx(-0.045359525568252, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.4622645372725116, rel=2e-5)
+    assert mf.c2.value == pytest.approx(1.807018272986906, rel=1e-5)
+    assert mf.c3.value == pytest.approx(2.3005632098784905, rel=1e-5)
+
+def test_05_fitting_test1_csv_ensemble_free():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 40, 60, 80, 100], labels=[1, 2, 3, 4])
+    dp = dr.process()
+    mf = pyWitness.ModelFitEnsemble(dp)
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(9.929160528690728, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 2
+    assert mf.chi2PerNDF == pytest.approx(4.964580264345364, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.006980880218575902, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(-0.00019209760974083547, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(0.7574113379383993, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.5445278559161522, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.4712095135660104, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(0.0047163119561311004, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(-0.0007144208090970518, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.106509748503039, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.193916803861908, rel=1e-5)
+    assert mf.c3.value == pytest.approx(1.371185767314882, rel=1e-5)
+    assert mf.c4.value == pytest.approx(1.7468725897881834, rel=1e-5)
 
-def test_05_fitting_test1_csv_ensemble():
+def test_05_fitting_test1_csv_integration_eqvar():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
     dr.collapseContinuousData(column="confidence")
     dp = dr.process()
-    mf_en = pyWitness.ModelFitEnsemble(dp)
+    mf = pyWitness.ModelFitIntegration(dp)
+    mf.setEqualVariance()
+    mf.fit()
 
-def test_05_fitting_test1_csv_integration():
+    assert mf.chi2 == pytest.approx(12.782436722859641, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 4
+    assert mf.chi2PerNDF == pytest.approx(3.1956091807149103, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.012389254262285876, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.5366281782007007, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(5.115694387174446e-05, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(5.115694387174446e-05, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.2251095944199202, rel=1e-5)
+    assert mf.c2.value == pytest.approx(2.764720963085291, rel=1e-5)
+    assert mf.c3.value == pytest.approx(4.786453224625339, rel=1e-5)
+
+def test_05_fitting_test1_csv_integration_uneqvar():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
     dr.collapseContinuousData(column="confidence")
     dp = dr.process()
-    mf_in = pyWitness.ModelFitIntegration(dp)
+    mf = pyWitness.ModelFitIntegration(dp)
+    mf.setUnequalVariance()
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(7.346304597247939, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 3
+    assert mf.chi2PerNDF == pytest.approx(2.448768199082646, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.06164187352643058, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(0.0, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(1.0, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(2.734951478496513, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(1.4440682177718327, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(4.567067698853903e-05, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(4.567067698853903e-05, rel=1e-5)
+    assert mf.c1.value == pytest.approx(1.281468457077398, rel=1e-5)
+    assert mf.c2.value == pytest.approx(2.900821480460631, rel=1e-5)
+    assert mf.c3.value == pytest.approx(5.06921554942873, rel=1e-5)
+
+def test_05_fitting_test1_csv_integration_free():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 40, 60, 80, 100], labels=[1, 2, 3, 4])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIntegration(dp)
+    mf.fit()
+
+    assert mf.chi2 == pytest.approx(10.222524151352737, rel=1e-5)
+    assert mf.numberDegreesOfFreedom == 2
+    assert mf.chi2PerNDF == pytest.approx(5.111262075676368, rel=1e-5)
+    assert mf.pValue == pytest.approx(0.006028469735381514, rel=1e-5)
+    assert mf.lureMean.value == pytest.approx(-0.0007710566698620955, rel=1e-5)
+    assert mf.lureSigma.value == pytest.approx(0.5920046766071095, rel=1e-5)
+    assert mf.targetMean.value == pytest.approx(1.6250990449599445, rel=1e-5)
+    assert mf.targetSigma.value == pytest.approx(0.8877215913533945, rel=1e-5)
+    assert mf.lureBetweenSigma.value == pytest.approx(-0.000838028225441163, rel=1e-5)
+    assert mf.targetBetweenSigma.value == pytest.approx(0.0004049627542837209, rel=1e-5)
+    assert mf.c1.value == pytest.approx(0.7498003652968959, rel=1e-5)
+    assert mf.c2.value == pytest.approx(1.079274299875618, rel=1e-5)
+    assert mf.c3.value == pytest.approx(1.7237076691036173, rel=1e-5)
+    assert mf.c4.value == pytest.approx(3.0151183915768636, rel=1e-5)
 
 def test_05_fitting_test1_csv_set_parameters_plot_hit_v_false():
     import pyWitness
diff --git a/test/test_06_adv_stats.py b/test/test_06_adv_stats.py
index dac336b..0e6b3df 100644
--- a/test/test_06_adv_stats.py
+++ b/test/test_06_adv_stats.py
@@ -1,3 +1,5 @@
+import pytest
+
 def test_adv_stats_test1_csv_bootstrap():
     import pyWitness
     dr = pyWitness.DataRaw("../data/tutorial/test1.csv")