adjust text on slides

_freeze/slides/week2-day2/execute-results/html.json

@@ -1,7 +1,7 @@
 {
-  "hash": "db2ea891d3216d93ef7995646005225a",
+  "hash": "c374be31af3eb796ed1aa21476d2dbdc",
   "result": {
-    "markdown": "---\ntitle: \"The Flaws of Averages\"\nformat: \n  revealjs:\n    theme: night\n    embed-resources: true\n    standalone: true\neditor: visual\nexecute: \n  echo: false\n---\n\n::: {.cell}\n\n:::\n\n\n##  {background-color=\"#2b6a6c\"}\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nSuppose...\n:::\n\n> \"Overall this instructor was educationally effective.\"\n\n. . .\n\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n<table class=\"table\" style=\"margin-left: auto; margin-right: auto;\">\n <thead>\n  <tr>\n   <th style=\"text-align:right;\"> year </th>\n   <th style=\"text-align:left;\"> quarter </th>\n   <th style=\"text-align:right;\"> average </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 2021 </td>\n   <td style=\"text-align:left;\"> Fall </td>\n   <td style=\"text-align:right;\"> 4.53 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2021 </td>\n   <td style=\"text-align:left;\"> Fall </td>\n   <td style=\"text-align:right;\"> 4.36 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Winter </td>\n   <td style=\"text-align:right;\"> 4.18 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Winter </td>\n   <td style=\"text-align:right;\"> 4.24 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.83 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.41 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.00 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n\n##  {background-color=\"#404040\"}\n\n<center>\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nHow were these averages calculated?\n:::\n</center>\n\n##  {background-color=\"#404040\"}\n\n<center>\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nWhat do these averages mean?\n:::\n</center>\n\n##\n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nThe Problem\n:::\n\n::: {style=\"font-size: 2em; color: #FFFFFF;\"}\nIt's incredibly rare for scientists, including statisticians, to explicitly think about that conditions underlying their models.\n:::\n\n<!-- Beyond “checking” higher level assumptions in a stale and automatic fashion.  -->\n\n<!-- I had many conversations in very different contexts with scientists about what -->\n\n<!-- the average calculated from the data (or mean in a model) could reasonably -->\n\n<!-- represent and whether that was really what the scientist was after.  -->\n\n##\n\n::: {style=\"font-size: 2.5em; color: #FFFFFF;\"}\nWhy so much resistance?\n:::\n\n::: {style=\"font-size: 0.75em; color: #FFFFFF;\"}\nDepartments hold specific expectations of statistics courses\n\n</br>\n\nThese expectations are conditional on the assumption that means represent the magic quantity of interest\n\n</br>\n\nI'm then expected to educate you to \"play the game\" in the scientific culture of averages\n:::\n\n##\n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nAveragarianism\n:::\n\n::: {style=\"font-size: 0.75em; color: #FFFFFF;\"}\n> \"The primary research method of averagarianism is aggregate, then analyze: First, combine many people together and look for patterns in the group. Then, use these group patterns (such as averages and other statistics) to analyze and model individuals. The science of the individual instead instructs scientists to analyze, then aggregate: First, look for pattern within each individual. Then, look for ways to combine these individual patterns into collective insight.\"\n>\n> The End of Average by Todd Rose\n\n:::\n\n##\n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nWhat else then?\n:::\n\n::: {.callout-tip}\n# 60-second exercise\nIf you could not use averages to evaluate, model, and select individuals, well then...what could you use?\n:::\n\n. . .\n\n</br> \n\nThe difficulty in responding to this question underscores how averagarianism has endured for so long and become so deeply ingrained throughout society.\n\n##\n\n::: {style=\"font-size: 2.5em; color: #FFFFFF;\"}\n\"We've always done it this way\"\n:::\n\nMethods based on averages are available, easy, convenient, and take little creativity --- and they are expected in our scientific culture.\n\n</br>\n\nJustification for using averages is simply not demanded --- though justification for use of anything but averages is incredibly difficult to sell.\n\n##  {background-color=\"#f48153\"}\n\n::: {style=\"font-size: 3.5em; color: #000000;\"}\nSome Rules to Play By\n:::\n\n. . .\n\n</br>\n\n::: {style=\"font-size: 0.75em; color: #000000;\"}\nLook at and understand your raw data before aggregating\n:::\n\n. . .\n\n</br>\n\n::: {style=\"font-size:0.75em; color: #000000;\"}\nBoxplots (and such) don't count as visualizing the raw data\n:::\n\n. . .\n\n</br>\n\n::: {style=\"font-size: 0.75em; color: #000000;\"}\nWe should only average things we are convinced are measuring the same thing\n:::\n\n",
+    "markdown": "---\ntitle: \"The Flaws of Averages\"\nformat: \n  revealjs:\n    theme: night\n    embed-resources: true\n    standalone: true\neditor: visual\nexecute: \n  echo: false\n---\n\n\n##  {background-color=\"#2b6a6c\"}\n\n\n::: {.cell}\n\n:::\n\n\n::: {style=\"font-size: 2em; color: #000000;\"}\nSuppose...\n:::\n\n> \"Overall this instructor was educationally effective.\"\n\n. . .\n\n\n::: {.cell}\n::: {.cell-output-display}\n`````{=html}\n<table class=\"table\" style=\"margin-left: auto; margin-right: auto;\">\n <thead>\n  <tr>\n   <th style=\"text-align:right;\"> year </th>\n   <th style=\"text-align:left;\"> quarter </th>\n   <th style=\"text-align:right;\"> average </th>\n  </tr>\n </thead>\n<tbody>\n  <tr>\n   <td style=\"text-align:right;\"> 2021 </td>\n   <td style=\"text-align:left;\"> Fall </td>\n   <td style=\"text-align:right;\"> 4.53 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2021 </td>\n   <td style=\"text-align:left;\"> Fall </td>\n   <td style=\"text-align:right;\"> 4.36 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Winter </td>\n   <td style=\"text-align:right;\"> 4.18 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Winter </td>\n   <td style=\"text-align:right;\"> 4.24 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.83 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.41 </td>\n  </tr>\n  <tr>\n   <td style=\"text-align:right;\"> 2022 </td>\n   <td style=\"text-align:left;\"> Spring </td>\n   <td style=\"text-align:right;\"> 4.00 </td>\n  </tr>\n</tbody>\n</table>\n\n`````\n:::\n:::\n\n\n##  {background-color=\"#404040\"}\n\n<center>\n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nHow were these averages calculated?\n:::\n\n</center>\n\n##  {background-color=\"#404040\"}\n\n<center>\n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nWhat do these averages mean?\n:::\n\n</center>\n\n## \n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nThe Problem\n:::\n\n::: {style=\"font-size: 2em; color: #FFFFFF;\"}\nIt's incredibly rare for scientists, including statisticians, to explicitly think about that conditions underlying their models.\n:::\n\n<!-- Beyond “checking” higher level assumptions in a stale and automatic fashion.  -->\n\n<!-- I had many conversations in very different contexts with scientists about what -->\n\n<!-- the average calculated from the data (or mean in a model) could reasonably -->\n\n<!-- represent and whether that was really what the scientist was after.  -->\n\n## \n\n::: {style=\"font-size: 2.5em; color: #FFFFFF;\"}\nWhy so much resistance?\n:::\n\n::: {style=\"font-size: 0.75em; color: #FFFFFF;\"}\nDepartments hold specific expectations of statistics courses\n\n</br>\n\nThese expectations are conditional on the assumption that means represent the magic quantity of interest\n\n</br>\n\nI'm then expected to educate you to \"play the game\" in the scientific culture of averages\n:::\n\n## \n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nAveragarianism\n:::\n\n::: {style=\"font-size: 0.75em; color: #FFFFFF;\"}\n> \"The primary research method of averagarianism is aggregate, then analyze: First, combine many people together and look for patterns in the group. Then, use these group patterns (such as averages and other statistics) to analyze and model individuals. The science of the individual instead instructs scientists to analyze, then aggregate: First, look for pattern within each individual. Then, look for ways to combine these individual patterns into collective insight.\"\n>\n> The End of Average by Todd Rose\n:::\n\n## \n\n::: {style=\"font-size: 3.5em; color: #FFFFFF;\"}\nWhat else then?\n:::\n\n::: callout-tip\n# 60-second exercise\n\nIf you could not use averages to evaluate, model, and select individuals, well then...what could you use?\n:::\n\n. . .\n\n</br>\n\nThe difficulty in responding to this question underscores how averagarianism has endured for so long and become so deeply ingrained throughout society.\n\n## \n\n::: {style=\"font-size: 2.5em; color: #FFFFFF;\"}\n\"We've always done it this way\"\n:::\n\nMethods based on averages are available, easy, convenient, and take little creativity --- and they are expected in our scientific culture.\n\n</br>\n\nJustification for using averages is simply not demanded --- though justification for use of anything but averages is incredibly difficult to sell.\n\n##  {background-color=\"#f48153\"}\n\n::: {style=\"font-size: 3.5em; color: #000000;\"}\nSome Rules to Play By\n:::\n\n. . .\n\n</br>\n\n::: {style=\"font-size: 0.75em; color: #000000;\"}\n- Look at and understand your raw data before aggregating\n:::\n\n. . .\n\n::: {style=\"font-size:0.75em; color: #000000;\"}\n- Boxplots (and such) don't count as visualizing the raw data\n:::\n\n. . .\n\n::: {style=\"font-size: 0.75em; color: #000000;\"}\n- We should only average things we are convinced are measuring the same thing\n:::\n",
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slides/week2-day2.qmd

@@ -10,13 +10,13 @@ execute:
   echo: false
 ---
 
+##  {background-color="#2b6a6c"}
+
 ```{r packages}
 library(tidyverse)
 library(kableExtra)
 ```
 
-##  {background-color="#2b6a6c"}
-
 ::: {style="font-size: 2em; color: #000000;"}
 Suppose...
 :::
@@ -44,20 +44,24 @@ kable(evals) %>%
 ##  {background-color="#404040"}
 
 <center>
+
 ::: {style="font-size: 3.5em; color: #FFFFFF;"}
 How were these averages calculated?
 :::
+
 </center>
 
 ##  {background-color="#404040"}
 
 <center>
+
 ::: {style="font-size: 3.5em; color: #FFFFFF;"}
 What do these averages mean?
 :::
+
 </center>
 
-##
+## 
 
 ::: {style="font-size: 3.5em; color: #FFFFFF;"}
 The Problem
@@ -75,7 +79,7 @@ It's incredibly rare for scientists, including statisticians, to explicitly thin
 
 <!-- represent and whether that was really what the scientist was after.  -->
 
-##
+## 
 
 ::: {style="font-size: 2.5em; color: #FFFFFF;"}
 Why so much resistance?
@@ -93,7 +97,7 @@ These expectations are conditional on the assumption that means represent the ma
 I'm then expected to educate you to "play the game" in the scientific culture of averages
 :::
 
-##
+## 
 
 ::: {style="font-size: 3.5em; color: #FFFFFF;"}
 Averagarianism
@@ -103,27 +107,27 @@ Averagarianism
 > "The primary research method of averagarianism is aggregate, then analyze: First, combine many people together and look for patterns in the group. Then, use these group patterns (such as averages and other statistics) to analyze and model individuals. The science of the individual instead instructs scientists to analyze, then aggregate: First, look for pattern within each individual. Then, look for ways to combine these individual patterns into collective insight."
 >
 > The End of Average by Todd Rose
-
 :::
 
-##
+## 
 
 ::: {style="font-size: 3.5em; color: #FFFFFF;"}
 What else then?
 :::
 
-::: {.callout-tip}
+::: callout-tip
 # 60-second exercise
+
 If you could not use averages to evaluate, model, and select individuals, well then...what could you use?
 :::
 
 . . .
 
-</br> 
+</br>
 
 The difficulty in responding to this question underscores how averagarianism has endured for so long and become so deeply ingrained throughout society.
 
-##
+## 
 
 ::: {style="font-size: 2.5em; color: #FFFFFF;"}
 "We've always done it this way"
@@ -146,22 +150,17 @@ Some Rules to Play By
 </br>
 
 ::: {style="font-size: 0.75em; color: #000000;"}
-Look at and understand your raw data before aggregating
+- Look at and understand your raw data before aggregating
 :::
 
 . . .
 
-</br>
-
 ::: {style="font-size:0.75em; color: #000000;"}
-Boxplots (and such) don't count as visualizing the raw data
+- Boxplots (and such) don't count as visualizing the raw data
 :::
 
 . . .
 
-</br>
-
 ::: {style="font-size: 0.75em; color: #000000;"}
-We should only average things we are convinced are measuring the same thing
+- We should only average things we are convinced are measuring the same thing
 :::
-