Render full book, add automated checks, and update dependency setup

Author: Lakens

.github/workflows/book-checks.yml

@@ -0,0 +1,34 @@
+name: Book Checks
+
+on:
+  push:
+    branches: [master, main]
+  pull_request:
+    branches: [master, main]
+  workflow_dispatch:
+
+jobs:
+  render-and-test:
+    runs-on: ubuntu-latest
+    timeout-minutes: 90
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up R
+        uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - name: Set up Quarto
+        uses: quarto-dev/quarto-actions/setup@v2
+
+      - name: Install R package dependencies
+        run: Rscript scripts/install_book_requirements.R
+
+      - name: Render book (HTML)
+        run: quarto render --to html
+
+      - name: Run book integrity tests
+        run: Rscript scripts/check_book_tests.R

01-pvalue.qmd

@@ -168,6 +168,7 @@ In the simulation, we generate n = 71 normally distributed IQ scores with means
 #| fig-cap: "Distribution of *p*-values when power = 50%." 
 
 #Set number of simulations
+set.seed(1605)
 nSims <- 100000 # number of simulated experiments
 p <- numeric(nSims) # set up empty variable to store all simulated *p*-values
 bars <- 20
@@ -225,6 +226,7 @@ When there is no true effect, and test assumptions are met, *p*-values for a *t*
 #| fig-cap: "Distribution of *p*-values when the null hypothesis is true."
 
 #Set number of simulations
+set.seed(1620)
 nSims <- 100000 #number of simulated experiments
 p <-numeric(nSims) #set up empty variable to store all simulated *p*-values
 
@@ -812,7 +814,7 @@ In our simulations, we know whether there is a true effect or not, but in the re
 ```{r, echo = FALSE, results = 'asis'}
 opts_p <- c(
    "The effect is significant, and provides strong support for the alternative hypothesis.",
-   answer = "The effect is significant, but it is without any doubt a Type 1 error.",
+   "The effect is significant, but it is without any doubt a Type 1 error.",
    "With high power, you should use an alpha level that is smaller than 0.05, and therefore, this effect cannot be considered significant. ", 
    answer = "The effect is significant, but the data are more likely under the null hypothesis than under the alternative hypothesis."
 )