diff --git a/assure-analyze/analysis.qmd b/assure-analyze/analysis.qmd
index 32c0fb7..67541c3 100644
--- a/assure-analyze/analysis.qmd
+++ b/assure-analyze/analysis.qmd
@@ -31,9 +31,10 @@ It is important to acknowledge this uncertainty up front to be transparent with
 Examples of how others have done this work include:
 
 -   Calif. Dept. of Finance Demographic Research Unit's [California Hard-to-Count Index Interactive Map](https://cacensus.maps.arcgis.com/apps/webappviewer/index.html?id=48be59de0ba94a3dacff1c9116df8b37), which measures the potential inaccuracies associated with relying on census data to enumerate demographic and socioeconomic characteristics in California
+
 -   Office of Environmental Health and Hazard Assessment [CalEnviroScreen 4.0 Race and Equity Analysis](https://storymaps.arcgis.com/stories/f555670d30a942e4b46b18293e2795a7)
 
-## Data Analysis Method Considerations
+### Data Analysis Method Considerations
 
 There are five main data analysis method types, and each has a different process, purpose, and interpretation (see table below). As you embark on your analysis, it's important to:
 
@@ -50,12 +51,12 @@ There are five main data analysis method types, and each has a different process
 | Prescriptive Analytics | Recommend actions or decisions.                                                                                    | The potential analytical methods are complex and require a large amount of quality and relevant data and computing power to be implemented appropriately. One should also consider the implications of the potential actions and decisions being considered using an equity lens. Striking the balance between data-driven insights and equity considerations is essential for advancing equity outcomes.  |
 | Causal Analytics       | Understand the cause and effect relationships between variables of interest.                                       | All causal analytical tools require strong assumptions and can never fully capture all of the context of the relationships in questions (i.e. extraneous variables that cannot be measured or assessed). If these methods are used, be sure to ground-truth the results with the communities the data you're using are meant to represent.                                                                 |
 
-# **Beware of common data fallacies**
+### **Beware of Common Data Fallacies**
 
-Beware of common data fallacies as you're interpreting your results and making conclusions:
+As with any analysis, we need to understand the limits of our data and the methods we use so we interpret the results we find appropriately. Beware of common data fallacies as you're interpreting your results and making conclusions:
 
 # Resources
 
 -   College of Water Informatics [Machine Learning Handbook](https://www.waterboards.ca.gov/resources/oima/cowi/machine_learning_handbook.html)
--   Flood M. D., Lemieux V. L., Varga M., and Wong B. L. W. (2016) '[The application of visual analytics to financial stability monitoring](https://doi.org/10.1016/j.jfs.2016.01.006)', Journal of Financial Stability, 27
+-   Flood M. D., Lemieux V. L., Varga M., and Wong B. L. W. (2016) '[The application of visual analytics to financial stability monitoring](https://doi.org/10.1016/j.jfs.2016.01.006)', Journal of Financial Stability
 -   DataCamp (2023) [The Data-Information-Knowledge-Wisdom Pyramid](https://www.datacamp.com/cheat-sheet/the-data-information-knowledge-wisdom-pyramid)