update exercise

docs/html/.doctrees/nbsphinx/exercises/exercise_resume_experiment.ipynb

@@ -90,7 +90,7 @@
     "\n",
     "Check for balance in terms of the average values of applicant gender (`female`), computer skills (`computerskills`), and years of experience (`yearsexp`) across the two arms of the experiment (i.e. by `black`). Calculate both the differences in means across treatment arms *and* test for statistical significance of these differences. Does gender, computer skills, and yearsexp look balanced across race groups in terms of both statistical significance and magnitude of difference?\n",
     "\n",
-    "Store the p-values associated with your t-test of these variables in `ex2_pvalue_female`, `ex2_pvalue_computerskills`, and `ex2_pvalue_yearsexp`.\n"
+    "Store the p-values associated with your t-test of these variables in `ex2_pvalue_female`, `ex2_pvalue_computerskills`, and `ex2_pvalue_yearsexp`. **Round your values to 2 decimal places.**\n"
    ]
   },
   {
@@ -112,7 +112,7 @@
     "\n",
     "Does education look balanced across racial groups?\n",
     "\n",
-    "Store the p-value from your chi squared test in results under the key `ex3_pvalue_education`."
+    "Store the p-value from your chi squared test in results under the key `ex3_pvalue_education`. **Please round to 2 decimal places.**"
    ]
   },
   {
@@ -145,9 +145,9 @@
     "\n",
     "Interpret your results—in both percentage *and* in percentage points, what is the effect of having a Black-sounding name (as opposed to a White-sounding name) on your resume?\n",
     "\n",
-    "Store how much more likely a White applicant is to receive a call back than a Black respondent in percentage and percentage points in `\"ex5_white_advantage_percent\"` and `\"ex5_white_advantage_percentage_points\"` (be sure to scale percentage points so a value of `1` corresponds to 1 percentage points).\n",
+    "Store how much more likely a White applicant is to receive a call back than a Black respondent in percentage and percentage points in `\"ex5_white_advantage_percent\"`and `\"ex5_white_advantage_percentage_points\"`. Please scale percentages so 1 is 1% and percentage points so a value of `1` corresponds to 1 percentage point. **Please round these answers to 2 decimal places.**\n",
     "\n",
-    "Store the p-value of the difference in `\"ex5_pvalue\"`"
+    "Store the p-value of the difference in `\"ex5_pvalue\"` **Please round your p-value to 5 decimal places.**"
    ]
   },
   {
@@ -165,7 +165,7 @@
     "\n",
     "How does this compare to the estimate you got above in exercise 5?\n",
     "\n",
-    "Store the p-value associated with `black` in `\"ex6_black_pvalue\"` (Don't round, obviously, or you won't get much!)"
+    "Store the p-value associated with `black` in `\"ex6_black_pvalue\"`. **Please round your pvalue to 5 decimal places.**"
    ]
   },
   {
@@ -209,7 +209,7 @@
     "- between White applicants without a college degree and Black applicants without a college degree (`ex8_black_nocollege`).\n",
     "- between White applicants with a college degree and Black applicants with a college degree (`ex8_black_college`).\n",
     "\n",
-    "Use negative values to denote a lower probability for Black applicants to get a call back. Scale so a value of `1` is a one percentage point difference.\n",
+    "Use negative values to denote a lower probability for Black applicants to get a call back. **Scale so a value of `1` is a one percentage point difference. Please round your answer to 2 percentage points.**\n",
     "\n",
     "Focus on the coefficient values, even if the significance is low."
    ]
@@ -301,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.8"
   },
   "vscode": {
    "interpreter": {

docs/html/_sources/exercises/exercise_resume_experiment.ipynb.txt

@@ -90,7 +90,7 @@
     "\n",
     "Check for balance in terms of the average values of applicant gender (`female`), computer skills (`computerskills`), and years of experience (`yearsexp`) across the two arms of the experiment (i.e. by `black`). Calculate both the differences in means across treatment arms *and* test for statistical significance of these differences. Does gender, computer skills, and yearsexp look balanced across race groups in terms of both statistical significance and magnitude of difference?\n",
     "\n",
-    "Store the p-values associated with your t-test of these variables in `ex2_pvalue_female`, `ex2_pvalue_computerskills`, and `ex2_pvalue_yearsexp`.\n"
+    "Store the p-values associated with your t-test of these variables in `ex2_pvalue_female`, `ex2_pvalue_computerskills`, and `ex2_pvalue_yearsexp`. **Round your values to 2 decimal places.**\n"
    ]
   },
   {
@@ -112,7 +112,7 @@
     "\n",
     "Does education look balanced across racial groups?\n",
     "\n",
-    "Store the p-value from your chi squared test in results under the key `ex3_pvalue_education`."
+    "Store the p-value from your chi squared test in results under the key `ex3_pvalue_education`. **Please round to 2 decimal places.**"
    ]
   },
   {
@@ -145,9 +145,9 @@
     "\n",
     "Interpret your results—in both percentage *and* in percentage points, what is the effect of having a Black-sounding name (as opposed to a White-sounding name) on your resume?\n",
     "\n",
-    "Store how much more likely a White applicant is to receive a call back than a Black respondent in percentage and percentage points in `\"ex5_white_advantage_percent\"` and `\"ex5_white_advantage_percentage_points\"` (be sure to scale percentage points so a value of `1` corresponds to 1 percentage points).\n",
+    "Store how much more likely a White applicant is to receive a call back than a Black respondent in percentage and percentage points in `\"ex5_white_advantage_percent\"`and `\"ex5_white_advantage_percentage_points\"`. Please scale percentages so 1 is 1% and percentage points so a value of `1` corresponds to 1 percentage point. **Please round these answers to 2 decimal places.**\n",
     "\n",
-    "Store the p-value of the difference in `\"ex5_pvalue\"`"
+    "Store the p-value of the difference in `\"ex5_pvalue\"` **Please round your p-value to 5 decimal places.**"
    ]
   },
   {
@@ -165,7 +165,7 @@
     "\n",
     "How does this compare to the estimate you got above in exercise 5?\n",
     "\n",
-    "Store the p-value associated with `black` in `\"ex6_black_pvalue\"` (Don't round, obviously, or you won't get much!)"
+    "Store the p-value associated with `black` in `\"ex6_black_pvalue\"`. **Please round your pvalue to 5 decimal places.**"
    ]
   },
   {
@@ -209,7 +209,7 @@
     "- between White applicants without a college degree and Black applicants without a college degree (`ex8_black_nocollege`).\n",
     "- between White applicants with a college degree and Black applicants with a college degree (`ex8_black_college`).\n",
     "\n",
-    "Use negative values to denote a lower probability for Black applicants to get a call back. Scale so a value of `1` is a one percentage point difference.\n",
+    "Use negative values to denote a lower probability for Black applicants to get a call back. **Scale so a value of `1` is a one percentage point difference. Please round your answer to 2 percentage points.**\n",
     "\n",
     "Focus on the coefficient values, even if the significance is low."
    ]
@@ -301,7 +301,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.8"
   },
   "vscode": {
    "interpreter": {