HLTH 501 BIOSTATISTICS FINAL EXAM 2021
EXAMINATION TEST 2026 FULL QUESTIONS
AND DETAILED SOLUTIONS GRADED A+
⩥ Descriptive statistics. Answer: Summarizing data, organizing data,
graphing, and describing numeric information. Concerned with measures
of central tendencies and measures of dispersion, as well as their graphic
presentation.
Refers to parameters used to describe the attributes of a set of data such
as mean, standard deviation, proportion, and rate.
⩥ Inferential statistics. Answer: Hypothesis testing and conclusion from
the sample. Make predictions or generalizations about a larger dataset
based on a sample from a smaller dataset.
⩥ Statistical methods are used to:. Answer: Measure and explain overall
variation, differentiate between random and meaningful variation, and
facilitate the interpretation of data
⩥ Sampling. Answer: Used to estimate or approximate the "real" or
"true" characteristics of the population under study.
,⩥ Sample. Answer: A smaller group or subset of the population selected
to investigate the association between exposure and disease in the larger
population.
⩥ Vignette. Answer: Used to apply statistical notions to real-life
scenarios.
⩥ Scales. Answer: Used to measure population characteristics.
⩥ Continuous variables. Answer: A measure that includes an infinite
number of values. Described with mean and standard deviation.
Examples: systolic blood pressure, height, BMI, age, temperature.
⩥ Discrete variables. Answer: Refers to only a few possible values,
measured in categories or classes. Described with a proportion or a
percentage and rate.
Examples: sex, race, marital status, skin color.
⩥ Ordinal variables. Answer: Refers to categorical measures with
natural order. Described using median or mode.
Examples: educational status, disease morbidity, SES.
, ⩥ Probability model. Answer: Used to infer the characteristics of the
population from the sample of inferential statistics, which attempts to
answer the question as to whether the observed difference is a result of
"chance."
⩥ Accuracy. Answer: How close the estimate is to the true value. The
ability of a measurement to be correct, on average.
⩥ Bias. Answer: The systematic or differential error in an estimate.
⩥ Selection bias. Answer: Occurs when the subjects selected for the
study are not representative of the entire source population, limiting how
study results can be generalized to the larger population.
⩥ Information/misclassification bias. Answer: Inaccuracy in the
observation or recording of measurements.
⩥ Precision. Answer: Refers to the variability in the estimate. Also
referred to as "random error."
Measured by the size of its confidence interval (CI).
EXAMINATION TEST 2026 FULL QUESTIONS
AND DETAILED SOLUTIONS GRADED A+
⩥ Descriptive statistics. Answer: Summarizing data, organizing data,
graphing, and describing numeric information. Concerned with measures
of central tendencies and measures of dispersion, as well as their graphic
presentation.
Refers to parameters used to describe the attributes of a set of data such
as mean, standard deviation, proportion, and rate.
⩥ Inferential statistics. Answer: Hypothesis testing and conclusion from
the sample. Make predictions or generalizations about a larger dataset
based on a sample from a smaller dataset.
⩥ Statistical methods are used to:. Answer: Measure and explain overall
variation, differentiate between random and meaningful variation, and
facilitate the interpretation of data
⩥ Sampling. Answer: Used to estimate or approximate the "real" or
"true" characteristics of the population under study.
,⩥ Sample. Answer: A smaller group or subset of the population selected
to investigate the association between exposure and disease in the larger
population.
⩥ Vignette. Answer: Used to apply statistical notions to real-life
scenarios.
⩥ Scales. Answer: Used to measure population characteristics.
⩥ Continuous variables. Answer: A measure that includes an infinite
number of values. Described with mean and standard deviation.
Examples: systolic blood pressure, height, BMI, age, temperature.
⩥ Discrete variables. Answer: Refers to only a few possible values,
measured in categories or classes. Described with a proportion or a
percentage and rate.
Examples: sex, race, marital status, skin color.
⩥ Ordinal variables. Answer: Refers to categorical measures with
natural order. Described using median or mode.
Examples: educational status, disease morbidity, SES.
, ⩥ Probability model. Answer: Used to infer the characteristics of the
population from the sample of inferential statistics, which attempts to
answer the question as to whether the observed difference is a result of
"chance."
⩥ Accuracy. Answer: How close the estimate is to the true value. The
ability of a measurement to be correct, on average.
⩥ Bias. Answer: The systematic or differential error in an estimate.
⩥ Selection bias. Answer: Occurs when the subjects selected for the
study are not representative of the entire source population, limiting how
study results can be generalized to the larger population.
⩥ Information/misclassification bias. Answer: Inaccuracy in the
observation or recording of measurements.
⩥ Precision. Answer: Refers to the variability in the estimate. Also
referred to as "random error."
Measured by the size of its confidence interval (CI).
