WGU C784 Applied Healthcare Statistics | 70 Questions with A+ Solutions
WGU C784
Applied Healthcare Statistics
Final Assessment Exam
UPDATED
Western Governors University | 100% Original & Verified A+ Solutions
Total Questions 70
Sections 10
Cognitive Mix 30% Recall / 50% Application / 20% Analysis
Format 60% Calculation / 40% Conceptual
Question Style 4-option MCQ, single best answer
Rationales A+ Solutions with step-by-step calculations
Coverage Descriptive Stats through Evidence-Based Practice
This examination covers WGU C784 Applied Healthcare Statistics at the objective assessment blueprint
level. Topics include descriptive vs. inferential statistics; variable types and levels of measurement
(nominal, ordinal, interval, ratio); sampling methods and study design (RCT, cohort, case-control);
measures of central tendency and variability; data visualization (histograms, box plots, scatter plots);
probability rules and Bayes' theorem with sensitivity, specificity, PPV, and NPV; binomial, Poisson, and
normal distributions; the Central Limit Theorem and standard error; confidence intervals for means and
proportions; hypothesis testing (null/alternative, Type I/II errors, p-values, power); statistical tests (t-tests,
ANOVA, chi-square, correlation, and regression); and healthcare applications including relative risk, odds
ratio, absolute risk reduction, number needed to treat, and critical appraisal of evidence. Each question is
followed by the verified correct answer and a 2-4 sentence rationale with step-by-step statistical reasoning
and healthcare context.
Examination Strategy: Read every stem carefully; identify whether the question is asking for a
calculation, an interpretation, or a study design critique. Apply the correct formula (e.g., z = (x - mu) /
sigma, CI = x-bar +/- z*sigma/sqrt(n), PPV = TP / (TP + FP), NNT = 1 / ARR) and verify the assumptions.
Aligned with WGU C784 course competencies and objective assessment blueprint
,Distractors represent common statistical errors including confusing Type I vs. Type II errors,
WGU
misinterpreting C784 Applied
p-values, confusingHealthcare Statistics
SD with SEM, | 70sensitivity/specificity,
reversing Questions with A+ treating
Solutions
correlation as
causation, and conflating statistical with clinical significance.
Aligned with WGU C784 course competencies and objective assessment blueprint
, WGU C784 Applied Healthcare Statistics | 2026/2027 Final Assessment Page 3
Section 1: Introduction to Statistics and Data Types
Descriptive vs. Inferential, Variables, & Levels of Measurement (Q1-Q7)
This section contains 7 questions aligned with introduction to statistics and data types. Review the rationale after answering;
rationales include step-by-step statistical reasoning and WGU C784 competency alignment.
Q1: A researcher reports that the average systolic blood pressure of a sample of 200 hospitalized adults is
128 mmHg, and uses this result to estimate the average systolic blood pressure of all hospitalized adults in
the United States. Which branch of statistics is being applied to make the estimate about the larger
population?
A. Descriptive statistics, because the sample mean summarizes the data.
B. Inferential statistics, because the sample result is generalized to a broader population. [CORRECT]
C. Probability theory, because the result depends on chance.
D. Bayesian inference, because prior distributions are required.
Correct Answer: B
Rationale: Descriptive statistics organize, summarize, and display data (e.g., calculating the sample mean of
128 mmHg); inferential statistics use sample data to draw conclusions or make generalizations about a larger
population. Using a sample mean to estimate a population parameter is a classic inferential task. Probability
theory concerns chance events, and Bayesian inference is a specific inferential approach requiring priors —
neither is the best general answer here.
Q2: A nurse collects data on patients' blood types (A, B, AB, O). Which of the following BEST describes the
variable type and level of measurement?
A. Quantitative variable measured at the ratio level, because blood types can be counted.
B. Qualitative (categorical) variable measured at the nominal level, because blood types are categories with
no inherent order. [CORRECT]
C. Qualitative variable measured at the ordinal level, because blood types can be ranked by frequency.
D. Quantitative variable measured at the interval level, because blood types are equally spaced.
Correct Answer: B
Rationale: Blood type is a qualitative (categorical) variable measured at the nominal level — categories have
names but no inherent numerical order or ranking. Ratio level requires a true zero (e.g., weight); ordinal
implies meaningful ranking (e.g., cancer stage I-IV); interval requires equal spacing without a true zero (e.g.,
temperature in Celsius). Blood types meet none of those criteria.
Q3: Which of the following is a continuous quantitative variable in a clinical setting?
A. Number of admissions to the ICU in a 24-hour period.
B. Patient's heart rate recorded in beats per minute. [CORRECT]
C. Blood type of a patient.
D. Whether a patient was readmitted within 30 days (Yes/No).
Correct Answer: B
Rationale: A continuous quantitative variable can take any value within a range, including decimals (e.g.,
heart rate could be 72.3 bpm if measured precisely, or values like 78.5). Number of ICU admissions is discrete
(whole numbers only). Blood type is categorical (nominal). Readmission status is a binary categorical variable.
Continuous variables typically come from measuring (e.g., heart rate, weight, blood pressure), whereas
discrete variables come from counting.
Western Governors University | Applied Healthcare Statistics | A+ Verified Solutions
70 Questions | Cognitive Mix: 30% Recall / 50% Application / 20% Analysis
WGU C784
Applied Healthcare Statistics
Final Assessment Exam
UPDATED
Western Governors University | 100% Original & Verified A+ Solutions
Total Questions 70
Sections 10
Cognitive Mix 30% Recall / 50% Application / 20% Analysis
Format 60% Calculation / 40% Conceptual
Question Style 4-option MCQ, single best answer
Rationales A+ Solutions with step-by-step calculations
Coverage Descriptive Stats through Evidence-Based Practice
This examination covers WGU C784 Applied Healthcare Statistics at the objective assessment blueprint
level. Topics include descriptive vs. inferential statistics; variable types and levels of measurement
(nominal, ordinal, interval, ratio); sampling methods and study design (RCT, cohort, case-control);
measures of central tendency and variability; data visualization (histograms, box plots, scatter plots);
probability rules and Bayes' theorem with sensitivity, specificity, PPV, and NPV; binomial, Poisson, and
normal distributions; the Central Limit Theorem and standard error; confidence intervals for means and
proportions; hypothesis testing (null/alternative, Type I/II errors, p-values, power); statistical tests (t-tests,
ANOVA, chi-square, correlation, and regression); and healthcare applications including relative risk, odds
ratio, absolute risk reduction, number needed to treat, and critical appraisal of evidence. Each question is
followed by the verified correct answer and a 2-4 sentence rationale with step-by-step statistical reasoning
and healthcare context.
Examination Strategy: Read every stem carefully; identify whether the question is asking for a
calculation, an interpretation, or a study design critique. Apply the correct formula (e.g., z = (x - mu) /
sigma, CI = x-bar +/- z*sigma/sqrt(n), PPV = TP / (TP + FP), NNT = 1 / ARR) and verify the assumptions.
Aligned with WGU C784 course competencies and objective assessment blueprint
,Distractors represent common statistical errors including confusing Type I vs. Type II errors,
WGU
misinterpreting C784 Applied
p-values, confusingHealthcare Statistics
SD with SEM, | 70sensitivity/specificity,
reversing Questions with A+ treating
Solutions
correlation as
causation, and conflating statistical with clinical significance.
Aligned with WGU C784 course competencies and objective assessment blueprint
, WGU C784 Applied Healthcare Statistics | 2026/2027 Final Assessment Page 3
Section 1: Introduction to Statistics and Data Types
Descriptive vs. Inferential, Variables, & Levels of Measurement (Q1-Q7)
This section contains 7 questions aligned with introduction to statistics and data types. Review the rationale after answering;
rationales include step-by-step statistical reasoning and WGU C784 competency alignment.
Q1: A researcher reports that the average systolic blood pressure of a sample of 200 hospitalized adults is
128 mmHg, and uses this result to estimate the average systolic blood pressure of all hospitalized adults in
the United States. Which branch of statistics is being applied to make the estimate about the larger
population?
A. Descriptive statistics, because the sample mean summarizes the data.
B. Inferential statistics, because the sample result is generalized to a broader population. [CORRECT]
C. Probability theory, because the result depends on chance.
D. Bayesian inference, because prior distributions are required.
Correct Answer: B
Rationale: Descriptive statistics organize, summarize, and display data (e.g., calculating the sample mean of
128 mmHg); inferential statistics use sample data to draw conclusions or make generalizations about a larger
population. Using a sample mean to estimate a population parameter is a classic inferential task. Probability
theory concerns chance events, and Bayesian inference is a specific inferential approach requiring priors —
neither is the best general answer here.
Q2: A nurse collects data on patients' blood types (A, B, AB, O). Which of the following BEST describes the
variable type and level of measurement?
A. Quantitative variable measured at the ratio level, because blood types can be counted.
B. Qualitative (categorical) variable measured at the nominal level, because blood types are categories with
no inherent order. [CORRECT]
C. Qualitative variable measured at the ordinal level, because blood types can be ranked by frequency.
D. Quantitative variable measured at the interval level, because blood types are equally spaced.
Correct Answer: B
Rationale: Blood type is a qualitative (categorical) variable measured at the nominal level — categories have
names but no inherent numerical order or ranking. Ratio level requires a true zero (e.g., weight); ordinal
implies meaningful ranking (e.g., cancer stage I-IV); interval requires equal spacing without a true zero (e.g.,
temperature in Celsius). Blood types meet none of those criteria.
Q3: Which of the following is a continuous quantitative variable in a clinical setting?
A. Number of admissions to the ICU in a 24-hour period.
B. Patient's heart rate recorded in beats per minute. [CORRECT]
C. Blood type of a patient.
D. Whether a patient was readmitted within 30 days (Yes/No).
Correct Answer: B
Rationale: A continuous quantitative variable can take any value within a range, including decimals (e.g.,
heart rate could be 72.3 bpm if measured precisely, or values like 78.5). Number of ICU admissions is discrete
(whole numbers only). Blood type is categorical (nominal). Readmission status is a binary categorical variable.
Continuous variables typically come from measuring (e.g., heart rate, weight, blood pressure), whereas
discrete variables come from counting.
Western Governors University | Applied Healthcare Statistics | A+ Verified Solutions
70 Questions | Cognitive Mix: 30% Recall / 50% Application / 20% Analysis
