diff --git a/00_Introduction.qmd b/00_Introduction.qmd
index 34bd408e8c71528e05a7f9dcd6fe809214d97b94..f84cce30bef499ccf33ba2c7e5e6f7365d0869a6 100644
--- a/00_Introduction.qmd
+++ b/00_Introduction.qmd
@@ -153,7 +153,7 @@ data bases
 - Apply basic data engineering techniques using Python in Jupyter Notebooks
 - Understand and apply basic data analytics techniques to create
 business value
-- Understand data fellacies and know how to avoid them 
+- Understand data fallacies and know how to avoid them 
 - Reflect on data ethics in a personal and business context
 
 ## Agenda
@@ -162,7 +162,7 @@ business value
 2. Data Insights Process
 3. Data Engineering
 4. Data Analysis
-5. Data Fellacies
+5. Data Fallacies
 6. Data Storytelling
 7. Data Ethics
 
diff --git a/05_data_fellacies.qmd b/05_data_fellacies.qmd
index 04c8bb4db94ebec59d9186342228cd3379f74070..c071c4a04fb09011be01ad772b133b922d3ebe4a 100644
--- a/05_data_fellacies.qmd
+++ b/05_data_fellacies.qmd
@@ -1,6 +1,6 @@
 ---
 title: "Data Literacy"
-subtitle: "Chapter 5: Data Fellacies"
+subtitle: "Chapter 5: Data Fallacies"
 author: Prof. Dr. Michael Bücker
 number-offset: [6,0]
 bibliography: references.bib
@@ -15,7 +15,7 @@ bibliography: references.bib
 
 
 
-# Data fellacies {background-color="#0014a0"}
+# Data fallacies {background-color="#0014a0"}
 
 ::: footer
 :::
@@ -23,7 +23,25 @@ bibliography: references.bib
 
 ## Definition
 
-# Exercise {background-color="#0014a0"}
+- Data fallacies are misleading conclusions drawn from data due to incorrect reasoning or flawed analysis methods.
+- They can result from improper data collection, misinterpretation of statistical significance, or failure to account for variables.
+- Common types include sampling bias, false causality, and overlooking Simpson's Paradox.
+- Such fallacies can lead to poor decision-making and misinformed policies.
+- Awareness and rigorous statistical testing are key to avoiding data fallacies.
+
+
+## Guidelines
+
+- Ensure rigorous statistical testing and data collection methods.
+- Be aware of common data fallacies and actively check for them in analysis.
+- Utilize randomized controlled trials to establish causation where possible.
+- Collect data that is representative of the entire population to avoid sampling bias.
+- Apply critical thinking and seek peer review to challenge and validate findings.
+- Visualize data to identify patterns or anomalies that summary statistics may miss.
+- Include context and consider external factors when interpreting ratios and percentages.
+- Remain skeptical of correlations until causation has been reliably demonstrated.
+
+
 
 # Examples {background-color="#0014a0"}
 
@@ -36,6 +54,11 @@ bibliography: references.bib
 :::: {.columns}
 
 ::: {.column width="47.5%"}
+- Survivorship bias is a logical error focusing on aspects that support surviving a process and overlooking those that did not due to lack of visibility.
+- Common in finance where funds with better performance are more visible than those performing poorly or those that have ceased to exist.
+- It can lead to overly optimistic beliefs because failures are ignored, such as in wartime machinery damage analysis where only returning aircraft were examined.
+- Often causes false conclusions by examining only "success" stories and not considering the unseen failures behind data points.
+- To avoid survivorship bias, it's important to look at the whole picture, including those elements or data sets that might have been excluded over time.
 
 :::
 
@@ -51,12 +74,18 @@ bibliography: references.bib
 
 
 
-## False causality
+## False causality and spurious correlation
 
 :::: {.columns}
 
 ::: {.column width="47.5%"}
-[https://www.tylervigen.com/spurious-correlations](https://www.tylervigen.com/spurious-correlations)
+
+- False causality refers to incorrectly assuming that a correlation between two variables implies one causes the other.
+- Coincidental timing can lead to assuming a cause-and-effect relationship where none exists.
+- It overlooks potential underlying factors or confounding variables that may influence the correlation.
+- Critical analysis with rigorous statistical methods is needed to establish true causality.
+
+
 :::
 
 ::: {.column width="5%"}
@@ -66,22 +95,108 @@ bibliography: references.bib
 ::: {.column width="47.5%"}
 ![Visualization of false causality (*Source:* [https://www.geckoboard.com/best-practice/statistical-fallacies/](https://www.geckoboard.com/best-practice/statistical-fallacies/))](img/fellacies_falsecausality.png){#fig-falsecausality}
 
+See also: [https://www.tylervigen.com/spurious-correlations](https://www.tylervigen.com/spurious-correlations)
+
+:::
+::::
+
+
+## Danger of summary statistics
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+- Anscombe's Quartet shows four datasets with identical summary statistics but different distributions.
+- Reliance on summary stats can be misleading as they may not capture data nuances.
+- Outliers and data distribution patterns are not evident in summary statistics.
+- Data visualization is essential alongside numerical summary to understand data fully.
+
+
+
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+![Visualization of the danger of summary statistics (*Source:* [https://www.geckoboard.com/best-practice/statistical-fallacies/](https://www.geckoboard.com/best-practice/statistical-fallacies/))](img/fellacies_summarymetrics.png){#fig-fellacies_summarymetrics}
+
+see also: [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/)
+
+:::
+::::
+
+
+## Simpson's Paradox
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+- Simpson's Paradox occurs when a trend appears in different groups of data but disappears or reverses when these groups are combined.
+- This paradox warns against combining data from multiple sources without accounting for potential lurking variables.
+- It demonstrates how aggregated data can mask or misrepresent the actual nature of the relationships within the data.
+- Careful examination of data subsets is necessary to ensure accurate interpretation of statistical results.
+
+
+
+
+:::
+
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+![Visualization of Simpson's Paradox (*Source:* [https://www.geckoboard.com/best-practice/statistical-fallacies/](https://www.geckoboard.com/best-practice/statistical-fallacies/))](img/fellacies_simpsonsparadox.png){#fig-fellacies_simpsonsparadox}
+
 :::
 ::::
 
 
 
-## Exercise 
 
-::: callout-caution
-## Exercise
+## Sampling bias
+
+:::: {.columns}
+
+::: {.column width="47.5%"}
+
+- Sampling bias occurs when a sample is not representative of the population as a whole.
+- It results in certain groups being overrepresented or underrepresented in the sample.
+- Common causes include non-random sampling, pre-selection criteria, and inaccessible segments of the population.
+- The bias skews the statistical analysis, leading to inaccurate conclusions.
+- To avoid sampling bias, random sampling and thorough design of the sampling process are crucial.
 
-Please analyze the following use case
 
 
 
 :::
 
+::: {.column width="5%"}
+
+:::
+
+::: {.column width="47.5%"}
+![Visualization of a sampling bias (*Source:* [https://www.geckoboard.com/best-practice/statistical-fallacies/](https://www.geckoboard.com/best-practice/statistical-fallacies/))](img/samplingbias.png){#fig-samplingbias}
+
+:::
+::::
+
+
+## Data fallacies when using ratios/percentages instead of absolute values
+
+- **Misleading Magnitudes**: Ratios and percentages can exaggerate the importance of changes in small data sets.
+- **Base Rate Neglect**: Interpretations may be skewed if the original quantities (base rates) involved are not considered.
+- **False Equivalence**: Ratios might imply a misleading equivalence between groups of vastly different sizes.
+- **Percentage Change Oversights**: Large percentage increases can occur from small absolute changes, and vice versa.
+- **Omitted Numerator/Denominator Context**: Without context on what's being divided, ratios can be meaningless or deceptive.
+- **Aggregation Fallacies**: Percentages of subgroups may not accurately represent the whole if the subgroup sizes are disproportionate.
+
+
+
 
 # References {.unnumbered .scrollable}
 
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diff --git a/output/00_Introduction.html b/output/00_Introduction.html
index f887020d8b920acf60629bd8efd64636b94f18a6..6b948a7c1dcc2065e7476fe5ee87894c0967d193 100644
--- a/output/00_Introduction.html
+++ b/output/00_Introduction.html
@@ -587,7 +587,7 @@ Chief Economist at Google</p>
 <li>Have an overview of data bases technologies and implementation of data bases</li>
 <li>Apply basic data engineering techniques using Python in Jupyter Notebooks</li>
 <li>Understand and apply basic data analytics techniques to create business value</li>
-<li>Understand data fellacies and know how to avoid them</li>
+<li>Understand data fallacies and know how to avoid them</li>
 <li>Reflect on data ethics in a personal and business context</li>
 </ul>
 </section>
@@ -598,7 +598,7 @@ Chief Economist at Google</p>
 <li>Data Insights Process</li>
 <li>Data Engineering</li>
 <li>Data Analysis</li>
-<li>Data Fellacies</li>
+<li>Data Fallacies</li>
 <li>Data Storytelling</li>
 <li>Data Ethics</li>
 </ol>
diff --git a/output/05_data_fellacies.html b/output/05_data_fellacies.html
index 45fa12b51449a735a669947160982edef6711155..b2c901fdff2739874e19ef23a290cf2eb3e83cc7 100644
--- a/output/05_data_fellacies.html
+++ b/output/05_data_fellacies.html
@@ -347,7 +347,7 @@
 
 <section id="title-slide" data-background-image="img/title.png" data-background-size="cover" class="quarto-title-block center">
   <h1 class="title">Data Literacy</h1>
-  <p class="subtitle">Chapter 6: Data Storytelling</p>
+  <p class="subtitle">Chapter 5: Data Fallacies</p>
 
 <div class="quarto-title-authors">
 <div class="quarto-title-author">
@@ -361,9 +361,8 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <nav role="doc-toc"> 
 <h2 id="toc-title">Table of contents</h2>
 <ul>
-<li><a href="#/data-storytelling" id="/toc-data-storytelling"><span class="header-section-number">6.1</span> Data storytelling</a></li>
-<li><a href="#/exercise" id="/toc-exercise"><span class="header-section-number">6.2</span> Exercise</a></li>
-<li><a href="#/examples" id="/toc-examples"><span class="header-section-number">6.3</span> Examples</a></li>
+<li><a href="#/data-fallacies" id="/toc-data-fallacies"><span class="header-section-number">6.1</span> Data fallacies</a></li>
+<li><a href="#/examples" id="/toc-examples"><span class="header-section-number">6.2</span> Examples</a></li>
 <li><a href="#/references" id="/toc-references">References</a></li>
 </ul>
 </nav>
@@ -371,29 +370,169 @@ Prof.&nbsp;Dr.&nbsp;Michael Bücker
 <section id="where-are-we" class="slide level3 unnumbered">
 <h3>Where are we?</h3>
 
-<img data-src="img/data_storytelling.png" class="r-stretch"></section>
+<img data-src="img/data_analysis.png" class="r-stretch"></section>
 <section>
-<section id="data-storytelling" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="6.1">
-<h2><span class="header-section-number">6.1</span> Data storytelling</h2>
+<section id="data-fallacies" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="6.1">
+<h2><span class="header-section-number">6.1</span> Data fallacies</h2>
 <div class="footer">
 
 </div>
 </section>
 <section id="definition" class="slide level3" data-number="6.1.1">
 <h3><span class="header-section-number">6.1.1</span> Definition</h3>
-</section></section>
-<section id="exercise" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="6.2">
-<h2><span class="header-section-number">6.2</span> Exercise</h2>
-
+<ul>
+<li>Data fallacies are misleading conclusions drawn from data due to incorrect reasoning or flawed analysis methods.</li>
+<li>They can result from improper data collection, misinterpretation of statistical significance, or failure to account for variables.</li>
+<li>Common types include sampling bias, false causality, and overlooking Simpson’s Paradox.</li>
+<li>Such fallacies can lead to poor decision-making and misinformed policies.</li>
+<li>Awareness and rigorous statistical testing are key to avoiding data fallacies.</li>
+</ul>
 </section>
-
-<section id="examples" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="6.3">
-<h2><span class="header-section-number">6.3</span> Examples</h2>
+<section id="guidelines" class="slide level3" data-number="6.1.2">
+<h3><span class="header-section-number">6.1.2</span> Guidelines</h3>
+<ul>
+<li>Ensure rigorous statistical testing and data collection methods.</li>
+<li>Be aware of common data fallacies and actively check for them in analysis.</li>
+<li>Utilize randomized controlled trials to establish causation where possible.</li>
+<li>Collect data that is representative of the entire population to avoid sampling bias.</li>
+<li>Apply critical thinking and seek peer review to challenge and validate findings.</li>
+<li>Visualize data to identify patterns or anomalies that summary statistics may miss.</li>
+<li>Include context and consider external factors when interpreting ratios and percentages.</li>
+<li>Remain skeptical of correlations until causation has been reliably demonstrated.</li>
+</ul>
+</section></section>
+<section>
+<section id="examples" class="title-slide slide level2 center" data-background-color="#0014a0" data-number="6.2">
+<h2><span class="header-section-number">6.2</span> Examples</h2>
 <div class="footer">
 
 </div>
 </section>
-
+<section id="survivorship-bias" class="slide level3" data-number="6.2.1">
+<h3><span class="header-section-number">6.2.1</span> Survivorship bias</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li>Survivorship bias is a logical error focusing on aspects that support surviving a process and overlooking those that did not due to lack of visibility.</li>
+<li>Common in finance where funds with better performance are more visible than those performing poorly or those that have ceased to exist.</li>
+<li>It can lead to overly optimistic beliefs because failures are ignored, such as in wartime machinery damage analysis where only returning aircraft were examined.</li>
+<li>Often causes false conclusions by examining only “success” stories and not considering the unseen failures behind data points.</li>
+<li>To avoid survivorship bias, it’s important to look at the whole picture, including those elements or data sets that might have been excluded over time.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<div id="fig-survivorshipbias" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="img/fellacies_survivorshipbias.png"></p>
+<figcaption>Figure&nbsp;6.1: Visualization of the survivorship bias (<em>Source:</em> <a href="https://www.geckoboard.com/best-practice/statistical-fallacies/">https://www.geckoboard.com/best-practice/statistical-fallacies/</a>)</figcaption>
+</figure>
+</div>
+</div>
+</div>
+</section>
+<section id="false-causality-and-spurious-correlation" class="slide level3" data-number="6.2.2">
+<h3><span class="header-section-number">6.2.2</span> False causality and spurious correlation</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li>False causality refers to incorrectly assuming that a correlation between two variables implies one causes the other.</li>
+<li>Coincidental timing can lead to assuming a cause-and-effect relationship where none exists.</li>
+<li>It overlooks potential underlying factors or confounding variables that may influence the correlation.</li>
+<li>Critical analysis with rigorous statistical methods is needed to establish true causality.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<div id="fig-falsecausality" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="img/fellacies_falsecausality.png"></p>
+<figcaption>Figure&nbsp;6.2: Visualization of false causality (<em>Source:</em> <a href="https://www.geckoboard.com/best-practice/statistical-fallacies/">https://www.geckoboard.com/best-practice/statistical-fallacies/</a>)</figcaption>
+</figure>
+</div>
+<p>See also: <a href="https://www.tylervigen.com/spurious-correlations">https://www.tylervigen.com/spurious-correlations</a></p>
+</div>
+</div>
+</section>
+<section id="danger-of-summary-statistics" class="slide level3" data-number="6.2.3">
+<h3><span class="header-section-number">6.2.3</span> Danger of summary statistics</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li>Anscombe’s Quartet shows four datasets with identical summary statistics but different distributions.</li>
+<li>Reliance on summary stats can be misleading as they may not capture data nuances.</li>
+<li>Outliers and data distribution patterns are not evident in summary statistics.</li>
+<li>Data visualization is essential alongside numerical summary to understand data fully.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<div id="fig-fellacies_summarymetrics" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="img/fellacies_summarymetrics.png"></p>
+<figcaption>Figure&nbsp;6.3: Visualization of the danger of summary statistics (<em>Source:</em> <a href="https://www.geckoboard.com/best-practice/statistical-fallacies/">https://www.geckoboard.com/best-practice/statistical-fallacies/</a>)</figcaption>
+</figure>
+</div>
+<p>see also: <a href="https://www.research.autodesk.com/publications/same-stats-different-graphs/">https://www.research.autodesk.com/publications/same-stats-different-graphs/</a></p>
+</div>
+</div>
+</section>
+<section id="simpsons-paradox" class="slide level3" data-number="6.2.4">
+<h3><span class="header-section-number">6.2.4</span> Simpson’s Paradox</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li>Simpson’s Paradox occurs when a trend appears in different groups of data but disappears or reverses when these groups are combined.</li>
+<li>This paradox warns against combining data from multiple sources without accounting for potential lurking variables.</li>
+<li>It demonstrates how aggregated data can mask or misrepresent the actual nature of the relationships within the data.</li>
+<li>Careful examination of data subsets is necessary to ensure accurate interpretation of statistical results.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<div id="fig-fellacies_simpsonsparadox" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="img/fellacies_simpsonsparadox.png"></p>
+<figcaption>Figure&nbsp;6.4: Visualization of Simpson’s Paradox (<em>Source:</em> <a href="https://www.geckoboard.com/best-practice/statistical-fallacies/">https://www.geckoboard.com/best-practice/statistical-fallacies/</a>)</figcaption>
+</figure>
+</div>
+</div>
+</div>
+</section>
+<section id="sampling-bias" class="slide level3" data-number="6.2.5">
+<h3><span class="header-section-number">6.2.5</span> Sampling bias</h3>
+<div class="columns">
+<div class="column" style="width:47.5%;">
+<ul>
+<li>Sampling bias occurs when a sample is not representative of the population as a whole.</li>
+<li>It results in certain groups being overrepresented or underrepresented in the sample.</li>
+<li>Common causes include non-random sampling, pre-selection criteria, and inaccessible segments of the population.</li>
+<li>The bias skews the statistical analysis, leading to inaccurate conclusions.</li>
+<li>To avoid sampling bias, random sampling and thorough design of the sampling process are crucial.</li>
+</ul>
+</div><div class="column" style="width:5%;">
+
+</div><div class="column" style="width:47.5%;">
+<div id="fig-samplingbias" class="quarto-figure quarto-figure-center">
+<figure>
+<p><img data-src="img/samplingbias.png"></p>
+<figcaption>Figure&nbsp;6.5: Visualization of a sampling bias (<em>Source:</em> <a href="https://www.geckoboard.com/best-practice/statistical-fallacies/">https://www.geckoboard.com/best-practice/statistical-fallacies/</a>)</figcaption>
+</figure>
+</div>
+</div>
+</div>
+</section>
+<section id="data-fallacies-when-using-ratiospercentages-instead-of-absolute-values" class="slide level3" data-number="6.2.6">
+<h3><span class="header-section-number">6.2.6</span> Data fallacies when using ratios/percentages instead of absolute values</h3>
+<ul>
+<li><strong>Misleading Magnitudes</strong>: Ratios and percentages can exaggerate the importance of changes in small data sets.</li>
+<li><strong>Base Rate Neglect</strong>: Interpretations may be skewed if the original quantities (base rates) involved are not considered.</li>
+<li><strong>False Equivalence</strong>: Ratios might imply a misleading equivalence between groups of vastly different sizes.</li>
+<li><strong>Percentage Change Oversights</strong>: Large percentage increases can occur from small absolute changes, and vice versa.</li>
+<li><strong>Omitted Numerator/Denominator Context</strong>: Without context on what’s being divided, ratios can be meaningless or deceptive.</li>
+<li><strong>Aggregation Fallacies</strong>: Percentages of subgroups may not accurately represent the whole if the subgroup sizes are disproportionate.</li>
+</ul>
+</section></section>
 <section id="references" class="title-slide slide level2 unnumbered scrollable smaller">
 <h2>References</h2>
 <div id="refs" role="list">
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