,Exam Blueprint & Instructions
WGU D599 Task 2: Statistical Analysis and Testing | Complete Report, Python Code & Hypothesis Testing
Part I · Exam Questions (1-200)
1. Statistical Analysis Fundamentals & Descriptive Stats (22 q) ....5
2. Hypothesis Testing, Null/Alternative & P-Values (23 q) ....6
3. T-Tests, ANOVA & Chi-Square Tests (22 q) ....7
4. Python Implementation: scipy.stats & statsmodels (22 q) ....8
5. Assumptions: Normality, Homogeneity & Independence (22 q) ....9
6. Confidence Intervals & Effect Sizes (22 q) . . . . 10
7. WGU D599 Task 2 Report Requirements & Documentation (22 q) . . . . 11
8. Visualization: Histograms, Boxplots & Q-Q Plots (22 q) . . . . 12
9. Business Implications, Limitations & Recommendations (23 q) . . . . 13
Instructions:
• 200 original questions for WGU D599 Task 2 mastery.
• Covers statistical analysis, hypothesis testing, t-tests, ANOVA, chi-square, effect sizes.
• Python uses scipy.stats, statsmodels, pandas per WGU standards.
• Correct answers and rationales follow each item immediately.
• Original study material, not actual WGU assessment.
,SECTION ONE: QUESTIONS 1-200
Choose the single best answer. Correct answers and rationales follow each item.
Statistical Analysis Fundamentals & Descriptive Stats
Question 1. In WGU D599 Task 2, what is the correct approach for calculating 95%
confidence interval for mean difference and interpreting practical significance?
A. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
B. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
C. Correct D599 Task 2 methodology: calculating 95% confidence interval for mean difference and
interpreting pr with hypothesis testing, Python modeling, and rubric-compliant documentation
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: C. Correct D599 Task 2 methodology: calculating 95% confidence interval for
mean difference and interpreting pr with hypothesis testing, Python modeling, and rubric-
compliant documentation
RATIONALE: For WGU D599 Task 2 statistical analysis, calculating 95% confidence interval for mean
difference and interpreting practical significance requires correct statistical methodology. Correct
(C): Correct D599 Task 2 methodology: calculating 95% confidence interval for mean difference and
interpreting pr with hypothesis testing, Python modeling, and rubric-compliant documentation. This
aligns with hypothesis testing theory, WGU rubric (Python code, hypothesis testing, report), and
avoids misinterpretation. Incorrect options cause Type I/II errors or rubric failure.
Question 2. When implementing applying chi-square test of independence to examine
association between two categorical variables in Python, which code avoids errors?
A. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
B. Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V
C. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: B. Use scipy.stats.chi2_contingency on contingency table, check expected
frequencies >=5, report chi2, df, p-value, and Cramer's V
RATIONALE: For WGU D599 Task 2 statistical analysis, applying chi-square test of independence to
examine association between two categorical variables requires correct statistical methodology.
Correct (B): Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V. This aligns with hypothesis testing theory, WGU rubric
(Python code, hypothesis testing, report), and avoids misinterpretation. Incorrect options cause
Type I/II errors or rubric failure.
Question 3. A student observes computing effect size using Cohen's d for t-tests and eta-
squared for ANOVA for practical importance with p=0.03. What is correct interpretation?
A. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
, B. Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal variances, report
t-statistic, df, p-value, and effect size per rubric
C. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: B. Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal
variances, report t-statistic, df, p-value, and effect size per rubric
RATIONALE: For WGU D599 Task 2 statistical analysis, computing effect size using Cohen's d for
t-tests and eta-squared for ANOVA for practical importance requires correct statistical methodology.
Correct (B): Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal variances, report
t-statistic, df, p-value, and effect size per rubric. This aligns with hypothesis testing theory,
WGU rubric (Python code, hypothesis testing, report), and avoids misinterpretation. Incorrect
options cause Type I/II errors or rubric failure.
Question 4. Which Python snippet correctly implements documenting Python code,
assumptions, test statistics, and business limitations per WGU D599 Task 2?
A. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
B. Correct D599 Task 2 methodology: documenting Python code, assumptions, test statistics, and
business limitat with hypothesis testing, Python modeling, and rubric-compliant documentation
C. Omit documentation of alpha level, assumptions, code, and business limitations from D599
Task 2 report
D. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
CORRECT ANSWER: B. Correct D599 Task 2 methodology: documenting Python code, assumptions,
test statistics, and business limitat with hypothesis testing, Python modeling, and rubric-
compliant documentation
RATIONALE: For WGU D599 Task 2 statistical analysis, documenting Python code, assumptions, test
statistics, and business limitations per WGU D599 Task 2 requires correct statistical methodology.
Correct (B): Correct D599 Task 2 methodology: documenting Python code, assumptions, test statistics,
and business limitat with hypothesis testing, Python modeling, and rubric-compliant documentation.
This aligns with hypothesis testing theory, WGU rubric (Python code, hypothesis testing, report),
and avoids misinterpretation. Incorrect options cause Type I/II errors or rubric failure.
Question 5. What is the primary purpose of applying chi-square test of independence to
examine association between two categorical variables in statistical analysis?
A. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
B. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
C. Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V
D. Omit documentation of alpha level, assumptions, code, and business limitations from D599
Task 2 report
CORRECT ANSWER: C. Use scipy.stats.chi2_contingency on contingency table, check expected
frequencies >=5, report chi2, df, p-value, and Cramer's V
RATIONALE: For WGU D599 Task 2 statistical analysis, applying chi-square test of independence to
examine association between two categorical variables requires correct statistical methodology.
Correct (C): Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V. This aligns with hypothesis testing theory, WGU rubric
WGU D599 Task 2: Statistical Analysis and Testing | Complete Report, Python Code & Hypothesis Testing
Part I · Exam Questions (1-200)
1. Statistical Analysis Fundamentals & Descriptive Stats (22 q) ....5
2. Hypothesis Testing, Null/Alternative & P-Values (23 q) ....6
3. T-Tests, ANOVA & Chi-Square Tests (22 q) ....7
4. Python Implementation: scipy.stats & statsmodels (22 q) ....8
5. Assumptions: Normality, Homogeneity & Independence (22 q) ....9
6. Confidence Intervals & Effect Sizes (22 q) . . . . 10
7. WGU D599 Task 2 Report Requirements & Documentation (22 q) . . . . 11
8. Visualization: Histograms, Boxplots & Q-Q Plots (22 q) . . . . 12
9. Business Implications, Limitations & Recommendations (23 q) . . . . 13
Instructions:
• 200 original questions for WGU D599 Task 2 mastery.
• Covers statistical analysis, hypothesis testing, t-tests, ANOVA, chi-square, effect sizes.
• Python uses scipy.stats, statsmodels, pandas per WGU standards.
• Correct answers and rationales follow each item immediately.
• Original study material, not actual WGU assessment.
,SECTION ONE: QUESTIONS 1-200
Choose the single best answer. Correct answers and rationales follow each item.
Statistical Analysis Fundamentals & Descriptive Stats
Question 1. In WGU D599 Task 2, what is the correct approach for calculating 95%
confidence interval for mean difference and interpreting practical significance?
A. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
B. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
C. Correct D599 Task 2 methodology: calculating 95% confidence interval for mean difference and
interpreting pr with hypothesis testing, Python modeling, and rubric-compliant documentation
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: C. Correct D599 Task 2 methodology: calculating 95% confidence interval for
mean difference and interpreting pr with hypothesis testing, Python modeling, and rubric-
compliant documentation
RATIONALE: For WGU D599 Task 2 statistical analysis, calculating 95% confidence interval for mean
difference and interpreting practical significance requires correct statistical methodology. Correct
(C): Correct D599 Task 2 methodology: calculating 95% confidence interval for mean difference and
interpreting pr with hypothesis testing, Python modeling, and rubric-compliant documentation. This
aligns with hypothesis testing theory, WGU rubric (Python code, hypothesis testing, report), and
avoids misinterpretation. Incorrect options cause Type I/II errors or rubric failure.
Question 2. When implementing applying chi-square test of independence to examine
association between two categorical variables in Python, which code avoids errors?
A. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
B. Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V
C. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: B. Use scipy.stats.chi2_contingency on contingency table, check expected
frequencies >=5, report chi2, df, p-value, and Cramer's V
RATIONALE: For WGU D599 Task 2 statistical analysis, applying chi-square test of independence to
examine association between two categorical variables requires correct statistical methodology.
Correct (B): Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V. This aligns with hypothesis testing theory, WGU rubric
(Python code, hypothesis testing, report), and avoids misinterpretation. Incorrect options cause
Type I/II errors or rubric failure.
Question 3. A student observes computing effect size using Cohen's d for t-tests and eta-
squared for ANOVA for practical importance with p=0.03. What is correct interpretation?
A. Interpret p=0.03 as 3% probability null is true and claim proof of alternative hypothesis
without effect size
, B. Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal variances, report
t-statistic, df, p-value, and effect size per rubric
C. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
D. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
CORRECT ANSWER: B. Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal
variances, report t-statistic, df, p-value, and effect size per rubric
RATIONALE: For WGU D599 Task 2 statistical analysis, computing effect size using Cohen's d for
t-tests and eta-squared for ANOVA for practical importance requires correct statistical methodology.
Correct (B): Use scipy.stats.ttest_ind or ttest_1samp, check normality and equal variances, report
t-statistic, df, p-value, and effect size per rubric. This aligns with hypothesis testing theory,
WGU rubric (Python code, hypothesis testing, report), and avoids misinterpretation. Incorrect
options cause Type I/II errors or rubric failure.
Question 4. Which Python snippet correctly implements documenting Python code,
assumptions, test statistics, and business limitations per WGU D599 Task 2?
A. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
B. Correct D599 Task 2 methodology: documenting Python code, assumptions, test statistics, and
business limitat with hypothesis testing, Python modeling, and rubric-compliant documentation
C. Omit documentation of alpha level, assumptions, code, and business limitations from D599
Task 2 report
D. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
CORRECT ANSWER: B. Correct D599 Task 2 methodology: documenting Python code, assumptions,
test statistics, and business limitat with hypothesis testing, Python modeling, and rubric-
compliant documentation
RATIONALE: For WGU D599 Task 2 statistical analysis, documenting Python code, assumptions, test
statistics, and business limitations per WGU D599 Task 2 requires correct statistical methodology.
Correct (B): Correct D599 Task 2 methodology: documenting Python code, assumptions, test statistics,
and business limitat with hypothesis testing, Python modeling, and rubric-compliant documentation.
This aligns with hypothesis testing theory, WGU rubric (Python code, hypothesis testing, report),
and avoids misinterpretation. Incorrect options cause Type I/II errors or rubric failure.
Question 5. What is the primary purpose of applying chi-square test of independence to
examine association between two categorical variables in statistical analysis?
A. Conduct t-test without checking normality or equal variances and report only p-value without
test statistic or assumptions
B. Use chi-square test with expected frequencies <5 and ignore assumption violations in WGU
report
C. Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V
D. Omit documentation of alpha level, assumptions, code, and business limitations from D599
Task 2 report
CORRECT ANSWER: C. Use scipy.stats.chi2_contingency on contingency table, check expected
frequencies >=5, report chi2, df, p-value, and Cramer's V
RATIONALE: For WGU D599 Task 2 statistical analysis, applying chi-square test of independence to
examine association between two categorical variables requires correct statistical methodology.
Correct (C): Use scipy.stats.chi2_contingency on contingency table, check expected frequencies >=5,
report chi2, df, p-value, and Cramer's V. This aligns with hypothesis testing theory, WGU rubric
