diff --git a/markdown/chapter_1_introduction/tutorial_5_results_and_samples.md b/markdown/chapter_1_introduction/tutorial_5_results_and_samples.md
index db2f4c3..bc38c57 100644
--- a/markdown/chapter_1_introduction/tutorial_5_results_and_samples.md
+++ b/markdown/chapter_1_introduction/tutorial_5_results_and_samples.md
@@ -333,655 +333,583 @@ print("The search has finished run - you may now continue the notebook.")
         folder for live output of the results.
         This Jupyter notebook cell with progress once the search has completed - this could take a few minutes!
         
-    2026-07-11 16:29:32,999 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores.
+    2026-07-11 18:13:35,792 - autofit.non_linear.search.abstract_search - INFO - Starting non-linear search with 1 cores.
 
 
-    2026-07-11 16:29:33,011 - tutorial_5_results_and_samples - INFO - The output path of this fit is HowToFit/output/chapter_1_introduction/tutorial_5_results_and_samples/f7b96c133609a46d15a374506304464c
+    2026-07-11 18:13:35,802 - tutorial_5_results_and_samples - INFO - The output path of this fit is HowToFit/output/chapter_1_introduction/tutorial_5_results_and_samples/f7b96c133609a46d15a374506304464c
 
 
-    2026-07-11 16:29:33,012 - tutorial_5_results_and_samples - INFO - Outputting pre-fit files (e.g. model.info, visualization).
+    2026-07-11 18:13:35,802 - tutorial_5_results_and_samples - INFO - Outputting pre-fit files (e.g. model.info, visualization).
 
 
-    2026-07-11 16:29:33,575 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores
+    2026-07-11 18:13:36,280 - autofit.non_linear.initializer - INFO - Generating initial samples of model using JAX LH Function cores
 
 
-    2026-07-11 16:29:33,597 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search
+    2026-07-11 18:13:36,298 - autofit.non_linear.initializer - INFO - Initial samples generated, starting non-linear search
 
 
-    2026-07-11 16:29:33,598 - tutorial_5_results_and_samples - INFO - Visualizing Starting Point Model in image_start folder.
+    2026-07-11 18:13:36,299 - tutorial_5_results_and_samples - INFO - Visualizing Starting Point Model in image_start folder.
 
 
-    2026-07-11 16:29:34,071 - tutorial_5_results_and_samples - INFO - Starting new Emcee non-linear search (no previous samples found).
+    2026-07-11 18:13:36,700 - tutorial_5_results_and_samples - INFO - Starting new Emcee non-linear search (no previous samples found).
 
 
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       log_likelihood -= np.sum(log_prior_list)
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+    /tmp/ipykernel_436/283143368.py:41: RuntimeWarning: overflow encountered in square
+      chi_squared_map = (residual_map / self.noise_map) ** 2.0
 
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-    2026-07-11 16:30:04,816 - tutorial_5_results_and_samples - INFO - Fit Running: Updating results (see output folder).
-
-
-    2026-07-11 16:30:05,229 - autofit.non_linear.samples.samples - INFO - Samples with weight less than 1e-10 removed from samples.csv.
-
-
-    2026-07-11 16:30:05,245 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF.
-
-
-    2026-07-11 16:30:06,204 - arviz - INFO - Found 'auto' as default backend, checking available backends
-
-
-    2026-07-11 16:30:06,205 - arviz - INFO - Matplotlib is available, defining as default backend
-
-
-    2026-07-11 16:30:06,210 - arviz - INFO - arviz_base 1.0.0 available, exposing its functions as part of the `arviz` namespace
+    2026-07-11 18:14:04,943 - tutorial_5_results_and_samples - INFO - Fit Running: Updating results (see output folder).
 
 
-    2026-07-11 16:30:06,410 - arviz - INFO - arviz_stats 1.0.0 available, exposing its functions as part of the `arviz` namespace
+    2026-07-11 18:14:05,339 - autofit.non_linear.samples.samples - INFO - Samples with weight less than 1e-10 removed from samples.csv.
 
 
-    2026-07-11 16:30:06,425 - arviz - INFO - arviz_plots 1.0.0 available, exposing its functions as part of the `arviz` namespace
+    2026-07-11 18:14:05,352 - autofit.non_linear.search.updater - INFO - Creating latent samples by drawing 100 from the PDF.
 
 
-    2026-07-11 16:30:06,657 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,434 - arviz - INFO - Found 'auto' as default backend, checking available backends
 
 
-    2026-07-11 16:30:06,691 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,435 - arviz - INFO - Matplotlib is available, defining as default backend
 
 
-    2026-07-11 16:30:06,714 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,440 - arviz - INFO - arviz_base 1.0.0 available, exposing its functions as part of the `arviz` namespace
 
 
-    2026-07-11 16:30:06,744 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,662 - arviz - INFO - arviz_stats 1.0.0 available, exposing its functions as part of the `arviz` namespace
 
 
-    2026-07-11 16:30:06,765 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,678 - arviz - INFO - arviz_plots 1.0.0 available, exposing its functions as part of the `arviz` namespace
 
 
-    2026-07-11 16:30:06,786 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,920 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:06,817 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:06,963 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:06,837 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:07,098 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:06,856 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:07,219 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:06,880 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:07,240 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:06,910 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:07,880 - tutorial_5_results_and_samples - INFO - Removing all files except for .zip file
 
 
-    2026-07-11 16:30:06,932 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:06,954 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:07,067 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:07,088 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:07,791 - tutorial_5_results_and_samples - INFO - Removing all files except for .zip file
-
-
-    2026-07-11 16:30:08,040 - tutorial_5_results_and_samples - INFO - Search complete, returning result
+    2026-07-11 18:14:08,109 - tutorial_5_results_and_samples - INFO - Search complete, returning result
 
 
     The search has finished run - you may now continue the notebook.
@@ -1002,7 +930,7 @@ It contains an `info` attribute which prints the result in readable format.
 print(result.info)
 ```
 
-    Maximum Log Likelihood                                                          157.53479909
+    Maximum Log Likelihood                                                          184.51147165
     
     model                                                                           Collection (N=6)
         gaussian                                                                    Gaussian (N=3)
@@ -1011,23 +939,23 @@ print(result.info)
     Maximum Log Likelihood Model:
     
     gaussian
-        centre                                                                      -294101561365780736.000
+        centre                                                                      49.937
     ... [15 lines of output truncated] ...
-        centre                                                                      50.10 (49.60, 157.08)
-        normalization                                                               51.74 (0.37, 54.07)
-        rate                                                                        0.06 (0.05, 1.15)
+        centre                                                                      49.84 (8.93, 50.81)
+        normalization                                                               29.09 (0.25, 52.60)
+        rate                                                                        0.06 (0.02, 3.90)
     
     
     Summary (1.0 sigma limits):
     
     gaussian
-        centre                                                                      50.14 (-10295526.34, 192063.19)
-        normalization                                                               8.73 (2.66, 78.09)
-        sigma                                                                       10.80 (-936478.33, 26484.73)
+        centre                                                                      50.12 (49.91, 50.41)
+        normalization                                                               41.40 (12.05, 85.47)
+        sigma                                                                       14.14 (13.09, 16.43)
     exponential
-        centre                                                                      50.10 (50.05, 50.15)
-        normalization                                                               51.74 (51.54, 51.92)
-        rate                                                                        0.06 (0.06, 0.06)
+        centre                                                                      49.84 (15.53, 50.10)
+        normalization                                                               29.09 (5.59, 45.67)
+        rate                                                                        0.06 (0.06, 0.09)
     
     instances
     
@@ -1056,7 +984,7 @@ print(samples)
 
     MCMC Samples: 
     
-    SamplesMCMC(500)
+    SamplesMCMC(450)
 
 
 __Parameters__
@@ -1080,9 +1008,9 @@ print(samples.parameter_lists[0][2], "\n")
 ```
 
     Sample 0's second parameter value (Gaussian -> normalization):
-    0.11335924010781939
+    71.10567165730815
     Sample 0's third parameter value (Gaussian -> sigma)
-    3099.028904610647 
+    19.041078224688448 
     
 
 
@@ -1115,9 +1043,9 @@ print(samples.weight_list[0])
 ```
 
     log(likelihood), log(prior), log(posterior) and weight of the first sample.
-    68.35981822308696
-    -1.7760622584685666
-    66.58375596461839
+    181.55217157376288
+    -6.882365196642858
+    174.66980637712
     1.0
 
 
@@ -1144,14 +1072,14 @@ print("Sigma = ", max_lh_instance.exponential.rate, "\n")
 ```
 
     Max Log Likelihood `Gaussian` Instance:
-    Centre =  -2.9410156136578074e+17
-    Normalization =  523915696.11279744
-    Sigma =  -3.023887584844618e+16 
+    Centre =  49.93679305733673
+    Normalization =  22.316040613432797
+    Sigma =  10.154534833581186 
     
     Max Log Likelihood Exponential Instance:
-    Centre =  50.2399798087453
-    Normalization =  51.65027804241896
-    Sigma =  0.0623539817262462 
+    Centre =  50.246449585900606
+    Normalization =  41.21960028703172
+    Sigma =  0.05184778273594719 
     
 
 
@@ -1168,7 +1096,7 @@ print(max_lh_vector, "\n\n")
 
     Max Log Likelihood Model Parameters: 
     
-    [-2.9410156136578074e+17, 523915696.11279744, -3.023887584844618e+16, 50.2399798087453, 51.65027804241896, 0.0623539817262462] 
+    [49.93679305733673, 22.316040613432797, 10.154534833581186, 50.246449585900606, 41.21960028703172, 0.05184778273594719] 
     
     
 
@@ -1230,14 +1158,14 @@ print("Sigma = ", median_pdf_instance.exponential.rate, "\n")
 ```
 
     Max Log Likelihood `Gaussian` Instance:
-    Centre =  50.13507900894478
-    Normalization =  8.730203550030652
-    Sigma =  10.797678770589716 
+    Centre =  50.11700527756945
+    Normalization =  41.400469983078125
+    Sigma =  14.142197776206366 
     
     Max Log Likelihood Exponential Instance:
-    Centre =  50.1016222399324
-    Normalization =  51.74042118842236
-    Sigma =  0.06213782821625456 
+    Centre =  49.8412364067728
+    Normalization =  29.08945386035243
+    Sigma =  0.06288372193481495 
     
 
 
@@ -1295,14 +1223,14 @@ print("Sigma = ", errors_at_lower_sigma_instance.gaussian.sigma, "\n")
 ```
 
     Upper Error values of Gaussian (at 3.0 sigma confidence):
-    Centre =  1.9035565111885748e+16
-    Normalization =  876330725.5982871
-    Sigma =  1954631025976492.2 
+    Centre =  43.05174582345312
+    Normalization =  50.462490200940366
+    Sigma =  44.6052208883872 
     
     lower Error values of Gaussian (at 3.0 sigma confidence):
-    Centre =  6.468665924711259e+17
-    Normalization =  8.730203390689214
-    Sigma =  6.650671449666327e+16 
+    Centre =  13.918276635649953
+    Normalization =  41.400075258873066
+    Sigma =  8.005905398848578 
     
 
 
@@ -1325,14 +1253,14 @@ print("Sigma = ", values_at_lower_sigma_instance.gaussian.sigma, "\n")
 ```
 
     Upper Parameter values w/ error of Gaussian (at 3.0 sigma confidence):
-    Centre =  1.90355651118858e+16
-    Normalization =  876330734.3284906
-    Sigma =  1954631025976503.0 
+    Centre =  93.16875110102256
+    Normalization =  91.86296018401849
+    Sigma =  58.74741866459357 
     
     lower Parameter values w/ errors of Gaussian (at 3.0 sigma confidence):
-    Centre =  -6.468665924711259e+17
-    Normalization =  1.5934143739195753e-07
-    Sigma =  -6.650671449666326e+16 
+    Centre =  36.198728641919494
+    Normalization =  0.0003947242050568983
+    Sigma =  6.136292377357789 
     
 
 
@@ -1346,54 +1274,24 @@ tool `corner.py`, which is wrapped via the `aplt.corner_cornerpy` function.
 aplt.corner_cornerpy(samples=result.samples)
 ```
 
-    2026-07-11 16:30:08,517 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,545 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,566 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,593 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:08,597 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:08,613 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:08,654 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:08,633 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:08,798 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:08,663 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:08,834 - root - WARNING - Too few points to create valid contours
 
 
-    2026-07-11 16:30:08,682 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,700 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,717 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,743 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,763 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,781 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,802 - root - WARNING - Too few points to create valid contours
-
-
-    2026-07-11 16:30:08,820 - root - WARNING - Too few points to create valid contours
+    2026-07-11 18:14:08,853 - root - WARNING - Too few points to create valid contours
 
 
 
     
-![png](tutorial_5_results_and_samples_files/tutorial_5_results_and_samples_33_15.png)
+![png](tutorial_5_results_and_samples_files/tutorial_5_results_and_samples_33_5.png)
     
 
 
@@ -1413,9 +1311,9 @@ print("Sigma = ", max_log_posterior_instance.gaussian.sigma, "\n")
 ```
 
     Maximum Log Posterior Vector:
-    Centre =  -24011277044876.77
-    Normalization =  3125270.2307336275
-    Sigma =  -2387925142098.1606 
+    Centre =  49.93679305733673
+    Normalization =  22.316040613432797
+    Sigma =  10.154534833581186 
     
 
 
@@ -1447,9 +1345,9 @@ print("Sigma = ", instance.gaussian.sigma, "\n")
 ```
 
     Gaussian Instance of last sample
-    Centre =  -2.5614444939533196e+16
-    Normalization =  798.0401817263084
-    Sigma =  -2627054060301375.5 
+    Centre =  49.12110332539849
+    Normalization =  22.855124856009187
+    Sigma =  10.199451132678083 
     
 
 
@@ -1506,7 +1404,7 @@ median_fwhm, lower_fwhm, upper_fwhm = af.marginalize(
 print(f"FWHM = {median_fwhm} ({upper_fwhm} {lower_fwhm}")
 ```
 
-    FWHM = 25.4265904087898 (6898791711474904.0 -6.302666628080957e+17
+    FWHM = 33.30233080420287 (141.4978563547001 14.097275235204755
 
 
 __Samples Filtering__
@@ -1539,12 +1437,12 @@ print(samples.max_log_likelihood(as_instance=False))
     Parameter paths in the model which are used for filtering:
     [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')]
     All parameters of the very first sample
-    [31513.97299772367, 0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713]
+    [48.65596967347352, 71.10567165730815, 19.041078224688448, 50.48493591145581, 13.710995372478395, 0.12390418007909251]
     All parameters of the very first sample (containing only the Gaussian centre.
-    [31513.97299772367]
+    [48.65596967347352]
     Maximum Log Likelihood Model Instances (containing only the Gaussian centre):
     
-    [-2.9410156136578074e+17]
+    [49.93679305733673]
 
 
 Above, we specified each path as a list of tuples of strings. 
@@ -1565,7 +1463,7 @@ print(samples.parameter_lists[0])
 ```
 
     All parameters of the very first sample (containing only the Gaussian centre).
-    [31513.97299772367]
+    [48.65596967347352]
 
 
 Above, we filtered the `Samples` but asking for all parameters which included the path ("gaussian", "centre").
@@ -1594,9 +1492,9 @@ print(samples.parameter_lists[0])
     Parameter paths in the model which are used for filtering:
     [('gaussian', 'centre'), ('gaussian', 'normalization'), ('gaussian', 'sigma'), ('exponential', 'centre'), ('exponential', 'normalization'), ('exponential', 'rate')]
     All parameters of the very first sample
-    [31513.97299772367, 0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713]
+    [48.65596967347352, 71.10567165730815, 19.041078224688448, 50.48493591145581, 13.710995372478395, 0.12390418007909251]
     All parameters of the very first sample (containing only the Gaussian normalization and sigma).
-    [0.11335924010781939, 3099.028904610647, 49.92329668729731, 52.104725511588285, 0.06232091297101713]
+    [71.10567165730815, 19.041078224688448, 50.48493591145581, 13.710995372478395, 0.12390418007909251]
 
 
 __Latex__
@@ -1626,10 +1524,14 @@ latex = af.text.Samples.latex(
 print(latex)
 ```
 
-    Example Prefix $x^{\rm{g}} = 50.14^{+19035565111885748.00}_{-646866592471125888.00}$ & $norm^{\rm{g}} = 8.73^{+876330725.60}_{-8.73}$ & $\sigma^{\rm{g}} = 10.80^{+1954631025976492.25}_{-66506714496663272.00}$ & $x^{\rm{e}} = 50.10^{+106.98}_{-0.50}$ & $norm^{\rm{e}} = 51.74^{+2.33}_{-51.37}$ & $\lambda^{\rm{e}} = 0.06^{+1.09}_{-0.01}$ \[-2pt]
+    Example Prefix $x^{\rm{g}} = 50.12^{+43.05}_{-13.92}$ & $norm^{\rm{g}} = 41.40^{+50.46}_{-41.40}$ & $\sigma^{\rm{g}} = 14.14^{+44.61}_{-8.01}$ & $x^{\rm{e}} = 49.84^{+0.97}_{-40.91}$ & $norm^{\rm{e}} = 29.09^{+23.51}_{-28.84}$ & $\lambda^{\rm{e}} = 0.06^{+3.84}_{-0.04}$ \[-2pt]
+
 
+__Wrap Up__
 
-Finish.
+This tutorial showed how to inspect the results of a model-fit: the maximum log likelihood instance, the full set of
+samples, parameter estimates with errors at a given confidence, and how to output quantities to a LaTeX table. These
+tools are the foundation for interpreting every model-fit you perform with **PyAutoFit**.
 
 
 ```python
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