diff --git a/.gitignore b/.gitignore
index 6a614e4700196b4417bff5d3d479c1b13b43ba1f..646b22721ee4d0a15416a72f866f761a84747a3b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,5 @@
 
 old
 
-.backup
-.log
-
+*.backup
+*.log
diff --git a/bf_materials.zip b/bf_materials.zip
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diff --git a/docs/guide.pdf b/docs/guide.pdf
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diff --git a/docs/presentation.Rmd b/docs/presentation.Rmd
index ac9fc67d7ecf91a21e07e969ae641c689f464720..2f4429bbfb5caf4a58b8396442b212fa98331a52 100644
--- a/docs/presentation.Rmd
+++ b/docs/presentation.Rmd
@@ -2,6 +2,7 @@
 title: "Introduction to Bayes factors"
 author: "Jeffrey R. Stevens"
 date: "9 May 2019"
+institute: "https://osf.io/h38sx/"
 output:
   beamer_presentation:
     theme: "CambridgeUS"
@@ -174,7 +175,9 @@ $\frac{P(D|H_0)}{P(D|H_1)}$
 \pause
 * $BF_{10}=1/BF_{01}$
 \pause
-* So $BF_{10}$=10 means that there is 10 times more evidence for $H_1$ than $H_0$ (and is equivalent to a $BF_{01}=0.1$)
+* $BF_{10}$=8 means that there is 8 times more evidence for $H_1$ than $H_0$ (and is equivalent to a $BF_{01}=1/8=0.125$)
+\pause
+* Bayes factors range from 0 to $\infty$
 
 ## Bayes factor cutoffs
 
@@ -245,6 +248,7 @@ $\frac{P(D|H_0)}{P(D|H_1)}$
     - Set pre-determined BF thresholds (e.g., 10 or 1/10)
     - Start with at least 20 samples per group
     - Adjust based on prior knowledge
+\pause
 * It is possible to conduct something like a Bayesian power analysis\footnote{\tiny{(Schönbrodt \& Wagenmakers, 2018, \emph{Psychonomic Bulletin \& Review})}}
 
 
@@ -279,15 +283,17 @@ $\frac{P(D|H_0)}{P(D|H_1)}$
 
 ## Model selection
 
-\Large
-If $BF_{10} = \frac{P(D|H_1)}{P(D|H_0)}$ and $BF_{20} = \frac{P(D|H_2)}{P(D|H_0)}$, 
+Let's say you are interested in comparing two models in a regression: $H_1$ and $H_2$. First, you will compare them to an intercept-only model $H_0$ to generate their Bayes factors:
 
-\pause
-then $BF_{12} = \frac{P(D|H_1)}{P(D|H_2)} = \frac{BF_{10}}{BF_{20}}$.
+\Large
+$BF_{10} = \frac{P(D|H_1)}{P(D|H_0)}$ and $BF_{20} = \frac{P(D|H_2)}{P(D|H_0)}$
 
 \pause
 \normalsize
-You can divide Bayes factors of two models (compared to null) to find the Bayes factor comparing those two models.
+Then, you can divide the Bayes factors of two models (compared to null) to find the Bayes factor comparing those two models:
+
+\Large
+$\frac{BF_{10}}{BF_{20}} = \frac{P(D|H_1)}{P(D|H_2)} = BF_{12}$
 
 ## Model selection
 
@@ -333,11 +339,15 @@ $BF_{interaction} = \frac{16}{14} = 1.14$
 
 * Methods
   - Define Bayes factor (I cite Wagenmakers, 2007)
-  - Describe levels of evidence (I cite Wagenmakers et al., 2018)
+  - Describe cutoffs for evidence (I cite Wagenmakers et al., 2018)
   - Describe priors/assumptions (I cite journal articles cited by packages)
 \pause
 * Results
   - Clarify direction (alternative/null)
+  - Describe effect in "levels of evidence" terms
+    + "There is moderate evidence for a difference between ..."
+    + "There is very strong evidence for no difference between ..."
+    + "There is no evidence for a difference between ..."
   - Give Bayes factors as you would p-values (use 1-2 decimal places for > 1; 2-3 decimal for < 1; cutoffs for > 100, < 0.01)
 \pause
 * Supplementary materials
@@ -354,7 +364,7 @@ $BF_{interaction} = \frac{16}{14} = 1.14$
 
 ## Take home
 \Large
-**With both JASP and R, BF is as easy as frequentist---and you can convert between them!**
+**With both JASP and R, Bayes factors are as easy as frequentist---and you can convert between them!**
 
 # Resources
 
diff --git a/docs/presentation.pdf b/docs/presentation.pdf
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