diff --git a/_data-science/includes/breadth-or-depth.md b/_data-science/includes/breadth-or-depth.md
index 4a6f8417..1f432066 100755
--- a/_data-science/includes/breadth-or-depth.md
+++ b/_data-science/includes/breadth-or-depth.md
@@ -49,7 +49,7 @@
     * *\dataDim* roughly equal to *\numData*?
     * Stratification of populations: batch effects etc.
 }
-\notes{Classical approaches to data analysis made use of many subjects to achieve statistical power. Traditionally, we measure a few things about many people. For example cardiac disease risks can be based on a limited number of factors inmany patients (such as whether the patient smokes, blood pressure, cholesterol levels etc). Because, traditionally, data matrices are stored with individuals in rows and features in columns[^depth-measurement], we refer to this as *depth* of measurement. In statistics this is sometimes known as the *large $p$, small $n$* domain because traditionally $p$ is used to denote the number of features we know about an individual and $n$ is used to denote the number of individuals. 
+\notes{Classical approaches to data analysis made use of many subjects to achieve statistical power. Traditionally, we measure a few things about many people. For example cardiac disease risks can be based on a limited number of factors in many patients (such as whether the patient smokes, blood pressure, cholesterol levels etc). Because, traditionally, data matrices are stored with individuals in rows and features in columns[^depth-measurement], we refer to this as *depth* of measurement. In statistics this is sometimes known as the *large $p$, small $n$* domain because traditionally $p$ is used to denote the number of features we know about an individual and $n$ is used to denote the number of individuals. 
 
 [^depth-measurement]: In statistics this is known as a *design matrix*, representing the design of a study. But in databases, one might think of each patient being in a row, or record of the database.}