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WGU C784 Applied Healthcare Statistics OA Exam
QUESTIONS AND VERIFIED ANSWERS LATEST
UPDATE THIS YEAR
POINT-FORM SUMMARIZED EXAM COVERAGE (WGU C784 Applied Healthcare Statistics)
• Statistics: collecting, organizing, summarizing, interpreting data to draw conclusions
• Population vs sample: entire pool versus subset used to represent whole
• Parameter: numerical summary of population characteristic
• Statistic: numerical summary of sample characteristic
• Descriptive statistics: summarizing and organizing sample data
• Inferential statistics: using sample data to draw conclusions about population
• Quantitative data: numerical values that can be counted or measured
• Qualitative data: categorical descriptions that cannot be measured numerically
• Discrete data: specific distinct values counted as whole numbers
• Continuous data: any value within an interval measured on a scale
• Nominal: categories with no order (blood type, gender)
• Ordinal: categories with meaningful order but unequal intervals (pain scale, education level)
• Interval: numerical order with equal intervals but no true zero (temperature in Celsius)
• Ratio: numerical order with equal intervals and true zero (height, weight, time)
• Independent variable: explanatory variable that may cause a result
• Dependent variable: response variable measured or observed
• Positive correlation: both variables move in same direction
• Negative correlation: variables move in opposite directions
• Correlation does NOT imply causation
• Confounding variable: influences both independent and dependent variables
• Probability: likelihood an event will occur, ranges 0 (impossible) to 1 (certain)
• Complement: probability event does NOT occur = 1 - P(A)
• Mutually exclusive events: cannot occur simultaneously, P(A and B) = 0
• Intersection: probability both events occur, P(A and B)
• Union: probability either event occurs, P(A or B) = P(A) + P(B) - P(A and B)
• Conditional probability: P(A|B) = P(A and B) / P(B)
• Independent events: P(A and B) = P(A) × P(B)
• Sensitivity: true positive rate, ability to identify those with disease
• Specificity: true negative rate, ability to identify those without disease
• Positive predictive value: probability disease present given positive test
• Negative predictive value: probability disease absent given negative test
• Prevalence: proportion of population with condition at specific time
• False positive rate = 1 - specificity
• False negative rate = 1 - sensitivity
• Random variable: variable whose values are numerical outcomes of random phenomenon
• Probability distribution: describes likelihood of each possible value
• Expected value: mean of probability distribution, long-run average
• Binomial distribution: number of successes in fixed number of independent trials
• Normal distribution: symmetric, bell-shaped, defined by mean and standard deviation
• Standard normal distribution: mean = 0, standard deviation = 1
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• Empirical rule (68-95-99.7): 68% within ±1σ, 95% within ±2σ, 99.7% within ±3σ
• Z-score = (x - μ)/σ, number of standard deviations from mean
• Mean: average, sum divided by count, most affected by outliers
• Median: middle value when ordered, resistant to outliers
• Mode: value that appears most frequently
• Variance: average squared deviation from mean
• Standard deviation: square root of variance, measure of spread
• Range: maximum minus minimum, simplest measure of dispersion
• Interquartile range (IQR) = Q3 - Q1, middle 50% of data
• Outlier: below Q1 - 1.5×IQR or above Q3 + 1.5×IQR
• Skewed right (positive): tail to right, mean > median
• Skewed left (negative): tail to left, mean < median
• Symmetric distribution: mean = median
• Histogram: graph for continuous data using adjacent bars
• Box plot: five-number summary (min, Q1, median, Q3, max)
• Scatterplot: graph for two quantitative variables
• Bar chart: graph for categorical data distribution
• Frequency distribution: table showing how often each value occurs
• Relative frequency: proportion = frequency / total
• Correlation coefficient (r): measures strength and direction of linear relationship, ranges -1 to +1
• Coefficient of determination (r²): proportion of variance in y explained by x
• Regression line: line of best fit, y = mx + b
• Slope (m) = change in y / change in x
• Y-intercept (b) = value of y when x = 0
• Residual: difference between observed and predicted values
• Extrapolation: predicting outside original data range (less reliable)
• Interpolation: estimating between known data points (more reliable)
• PEMDAS: Parentheses, Exponents, Multiplication/Division (left to right), Addition/Subtraction
(left to right)
• Exponent: power showing how many times base is multiplied
• Square root: number that produces specified number when multiplied by itself
• Prime number: positive integer with exactly two factors (1 and itself)
• Composite number: has more than two factors
• Integer: whole numbers positive, negative, or zero
• Rational number: can be expressed as ratio of integers
• Fraction: numerator/denominator, proper if numerator < denominator
• Mixed number: whole number plus proper fraction
• Least common denominator: smallest common multiple of denominators
• Percent: proportion per hundred, out of 100
• Percent increase = (new - original)/original × 100%
• Percent decrease = (original - new)/original × 100%
• Essential conversions: 1 kg = 2.2 pounds, 1000 mg = 1 g, 1000 mcg = 1 mg
• Celsius to Fahrenheit: F = (C × 9/5) + 32
• Fahrenheit to Celsius: C = (F - 32) × 5/9
• Rounding: method of estimating to make number easier to work with
• Significant figures: digits that carry meaning contributing to measurement precision
• Weighted average: accounts for different importance of values
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• Confidence interval: range of plausible values for population parameter
• Confidence level: long-run proportion of intervals containing parameter
• Margin of error: half-width of confidence interval
• Hypothesis testing: determines if evidence supports claim about population
• Null hypothesis (H₀): statement of no effect or no difference
• Alternative hypothesis (H₁): statement contradicting null
• P-value: probability of observed results if null hypothesis true
• Significance level (α): threshold for rejecting H₀, commonly 0.05
• If p-value < α: reject H₀ (statistically significant)
• If p-value ≥ α: fail to reject H₀ (not statistically significant)
• Type I error: rejecting true null hypothesis (false positive)
• Type II error: failing to reject false null hypothesis (false negative)
• Power = 1 - β, probability of correctly rejecting false null
• One-tailed test: directional alternative hypothesis
• Two-tailed test: non-directional alternative hypothesis
• Central limit theorem: sampling distribution of mean approximates normal for large n
• Standard error: standard deviation of sampling distribution
• Sampling bias: sample systematically differs from population
• Random sampling: each member has equal chance of selection, reduces bias
• Stratified sampling: divide population into subgroups then sample each
• Cluster sampling: randomly select groups then sample all within selected groups
• Systematic sampling: select every kth element from ordered list
• Convenience sampling: readily available participants (high bias risk)
• Law of large numbers: sample mean approaches population mean as sample size grows
• Measurement error: collected value differs from true value
• Reliability: consistency of measurement
• Validity: accuracy of measurement (measures what it intends)
• Frequency table: shows count and percentage for each category
• Contingency table: displays relationship between two categorical variables
• Relative risk: ratio of probabilities, used in cohort studies
• Odds ratio: ratio of odds, used in case-control studies
• Absolute risk reduction: difference in event rates
• Number needed to treat (NNT) = 1 / absolute risk reduction
• Incidence: new cases occurring in specified time period
• Mortality rate: number of deaths per population over time
• Morbidity rate: number of cases of disease per population
• Age-adjusted rate: removes age distribution effects for comparison
• Life expectancy: average remaining years of life at given age
• QALY: quality-adjusted life year, incorporates length and quality of life
• DALY: disability-adjusted life year, measures overall disease burden
• Evidence-based practice: integrates best research with clinical expertise and patient values
• Clinical significance: whether effect size matters in practice
• Forest plot: displays results from multiple studies in meta-analysis
• Funnel plot: assesses publication bias in meta-analysis
• Heterogeneity: variation in study results beyond chance
• I² statistic: quantifies proportion of variation due to heterogeneity
• Confounding variable obscures true relationship between variables
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• Interaction: effect of one variable depends on level of another
• Simpson's paradox: association reverses when subgroups are combined
• Data transformation: addresses non-normality or unequal variance
• Log transformation: often used for right-skewed data
• Nonparametric tests: used when parametric assumptions violated
• Mann-Whitney U: alternative to independent t-test
• Wilcoxon signed-rank: alternative to paired t-test
• Kruskal-Wallis: alternative to one-way ANOVA
• Bonferroni correction: αadjusted = α / number of comparisons
• Bootstrapping: uses resampling to estimate sampling distribution
• Bayesian statistics: incorporates prior information with observed data
• Bayes' theorem: P(A|B) = P(B|A) × P(A) / P(B)
• Control chart: distinguishes common cause from special cause variation
• Run chart: shows data over time with median reference line
• Statistical process control: monitors quality in healthcare operations
• HIPAA: protects patient privacy and confidentiality
• De-identified data: removes personal identifiers for research use
• Institutional review board (IRB): protects human subjects in research
• Informed consent: participants informed of risks and benefits before enrolling
1. A researcher wants to study the average blood pressure of all adult patients admitted to a large
hospital system over the past year. Instead of measuring every patient, the researcher measures a
representative subset. What is the entire group of all adult patients called in this study?
A) Sample
B) Statistic
C) Population
D) Parameter
Answer: C – The population is the entire group of interest (all adult patients); a sample is the subset
actually measured.
WGU C784 Applied Healthcare Statistics OA Exam
QUESTIONS AND VERIFIED ANSWERS LATEST
UPDATE THIS YEAR
POINT-FORM SUMMARIZED EXAM COVERAGE (WGU C784 Applied Healthcare Statistics)
• Statistics: collecting, organizing, summarizing, interpreting data to draw conclusions
• Population vs sample: entire pool versus subset used to represent whole
• Parameter: numerical summary of population characteristic
• Statistic: numerical summary of sample characteristic
• Descriptive statistics: summarizing and organizing sample data
• Inferential statistics: using sample data to draw conclusions about population
• Quantitative data: numerical values that can be counted or measured
• Qualitative data: categorical descriptions that cannot be measured numerically
• Discrete data: specific distinct values counted as whole numbers
• Continuous data: any value within an interval measured on a scale
• Nominal: categories with no order (blood type, gender)
• Ordinal: categories with meaningful order but unequal intervals (pain scale, education level)
• Interval: numerical order with equal intervals but no true zero (temperature in Celsius)
• Ratio: numerical order with equal intervals and true zero (height, weight, time)
• Independent variable: explanatory variable that may cause a result
• Dependent variable: response variable measured or observed
• Positive correlation: both variables move in same direction
• Negative correlation: variables move in opposite directions
• Correlation does NOT imply causation
• Confounding variable: influences both independent and dependent variables
• Probability: likelihood an event will occur, ranges 0 (impossible) to 1 (certain)
• Complement: probability event does NOT occur = 1 - P(A)
• Mutually exclusive events: cannot occur simultaneously, P(A and B) = 0
• Intersection: probability both events occur, P(A and B)
• Union: probability either event occurs, P(A or B) = P(A) + P(B) - P(A and B)
• Conditional probability: P(A|B) = P(A and B) / P(B)
• Independent events: P(A and B) = P(A) × P(B)
• Sensitivity: true positive rate, ability to identify those with disease
• Specificity: true negative rate, ability to identify those without disease
• Positive predictive value: probability disease present given positive test
• Negative predictive value: probability disease absent given negative test
• Prevalence: proportion of population with condition at specific time
• False positive rate = 1 - specificity
• False negative rate = 1 - sensitivity
• Random variable: variable whose values are numerical outcomes of random phenomenon
• Probability distribution: describes likelihood of each possible value
• Expected value: mean of probability distribution, long-run average
• Binomial distribution: number of successes in fixed number of independent trials
• Normal distribution: symmetric, bell-shaped, defined by mean and standard deviation
• Standard normal distribution: mean = 0, standard deviation = 1
, Page 2 of 127
• Empirical rule (68-95-99.7): 68% within ±1σ, 95% within ±2σ, 99.7% within ±3σ
• Z-score = (x - μ)/σ, number of standard deviations from mean
• Mean: average, sum divided by count, most affected by outliers
• Median: middle value when ordered, resistant to outliers
• Mode: value that appears most frequently
• Variance: average squared deviation from mean
• Standard deviation: square root of variance, measure of spread
• Range: maximum minus minimum, simplest measure of dispersion
• Interquartile range (IQR) = Q3 - Q1, middle 50% of data
• Outlier: below Q1 - 1.5×IQR or above Q3 + 1.5×IQR
• Skewed right (positive): tail to right, mean > median
• Skewed left (negative): tail to left, mean < median
• Symmetric distribution: mean = median
• Histogram: graph for continuous data using adjacent bars
• Box plot: five-number summary (min, Q1, median, Q3, max)
• Scatterplot: graph for two quantitative variables
• Bar chart: graph for categorical data distribution
• Frequency distribution: table showing how often each value occurs
• Relative frequency: proportion = frequency / total
• Correlation coefficient (r): measures strength and direction of linear relationship, ranges -1 to +1
• Coefficient of determination (r²): proportion of variance in y explained by x
• Regression line: line of best fit, y = mx + b
• Slope (m) = change in y / change in x
• Y-intercept (b) = value of y when x = 0
• Residual: difference between observed and predicted values
• Extrapolation: predicting outside original data range (less reliable)
• Interpolation: estimating between known data points (more reliable)
• PEMDAS: Parentheses, Exponents, Multiplication/Division (left to right), Addition/Subtraction
(left to right)
• Exponent: power showing how many times base is multiplied
• Square root: number that produces specified number when multiplied by itself
• Prime number: positive integer with exactly two factors (1 and itself)
• Composite number: has more than two factors
• Integer: whole numbers positive, negative, or zero
• Rational number: can be expressed as ratio of integers
• Fraction: numerator/denominator, proper if numerator < denominator
• Mixed number: whole number plus proper fraction
• Least common denominator: smallest common multiple of denominators
• Percent: proportion per hundred, out of 100
• Percent increase = (new - original)/original × 100%
• Percent decrease = (original - new)/original × 100%
• Essential conversions: 1 kg = 2.2 pounds, 1000 mg = 1 g, 1000 mcg = 1 mg
• Celsius to Fahrenheit: F = (C × 9/5) + 32
• Fahrenheit to Celsius: C = (F - 32) × 5/9
• Rounding: method of estimating to make number easier to work with
• Significant figures: digits that carry meaning contributing to measurement precision
• Weighted average: accounts for different importance of values
, Page 3 of 127
• Confidence interval: range of plausible values for population parameter
• Confidence level: long-run proportion of intervals containing parameter
• Margin of error: half-width of confidence interval
• Hypothesis testing: determines if evidence supports claim about population
• Null hypothesis (H₀): statement of no effect or no difference
• Alternative hypothesis (H₁): statement contradicting null
• P-value: probability of observed results if null hypothesis true
• Significance level (α): threshold for rejecting H₀, commonly 0.05
• If p-value < α: reject H₀ (statistically significant)
• If p-value ≥ α: fail to reject H₀ (not statistically significant)
• Type I error: rejecting true null hypothesis (false positive)
• Type II error: failing to reject false null hypothesis (false negative)
• Power = 1 - β, probability of correctly rejecting false null
• One-tailed test: directional alternative hypothesis
• Two-tailed test: non-directional alternative hypothesis
• Central limit theorem: sampling distribution of mean approximates normal for large n
• Standard error: standard deviation of sampling distribution
• Sampling bias: sample systematically differs from population
• Random sampling: each member has equal chance of selection, reduces bias
• Stratified sampling: divide population into subgroups then sample each
• Cluster sampling: randomly select groups then sample all within selected groups
• Systematic sampling: select every kth element from ordered list
• Convenience sampling: readily available participants (high bias risk)
• Law of large numbers: sample mean approaches population mean as sample size grows
• Measurement error: collected value differs from true value
• Reliability: consistency of measurement
• Validity: accuracy of measurement (measures what it intends)
• Frequency table: shows count and percentage for each category
• Contingency table: displays relationship between two categorical variables
• Relative risk: ratio of probabilities, used in cohort studies
• Odds ratio: ratio of odds, used in case-control studies
• Absolute risk reduction: difference in event rates
• Number needed to treat (NNT) = 1 / absolute risk reduction
• Incidence: new cases occurring in specified time period
• Mortality rate: number of deaths per population over time
• Morbidity rate: number of cases of disease per population
• Age-adjusted rate: removes age distribution effects for comparison
• Life expectancy: average remaining years of life at given age
• QALY: quality-adjusted life year, incorporates length and quality of life
• DALY: disability-adjusted life year, measures overall disease burden
• Evidence-based practice: integrates best research with clinical expertise and patient values
• Clinical significance: whether effect size matters in practice
• Forest plot: displays results from multiple studies in meta-analysis
• Funnel plot: assesses publication bias in meta-analysis
• Heterogeneity: variation in study results beyond chance
• I² statistic: quantifies proportion of variation due to heterogeneity
• Confounding variable obscures true relationship between variables
, Page 4 of 127
• Interaction: effect of one variable depends on level of another
• Simpson's paradox: association reverses when subgroups are combined
• Data transformation: addresses non-normality or unequal variance
• Log transformation: often used for right-skewed data
• Nonparametric tests: used when parametric assumptions violated
• Mann-Whitney U: alternative to independent t-test
• Wilcoxon signed-rank: alternative to paired t-test
• Kruskal-Wallis: alternative to one-way ANOVA
• Bonferroni correction: αadjusted = α / number of comparisons
• Bootstrapping: uses resampling to estimate sampling distribution
• Bayesian statistics: incorporates prior information with observed data
• Bayes' theorem: P(A|B) = P(B|A) × P(A) / P(B)
• Control chart: distinguishes common cause from special cause variation
• Run chart: shows data over time with median reference line
• Statistical process control: monitors quality in healthcare operations
• HIPAA: protects patient privacy and confidentiality
• De-identified data: removes personal identifiers for research use
• Institutional review board (IRB): protects human subjects in research
• Informed consent: participants informed of risks and benefits before enrolling
1. A researcher wants to study the average blood pressure of all adult patients admitted to a large
hospital system over the past year. Instead of measuring every patient, the researcher measures a
representative subset. What is the entire group of all adult patients called in this study?
A) Sample
B) Statistic
C) Population
D) Parameter
Answer: C – The population is the entire group of interest (all adult patients); a sample is the subset
actually measured.
