Applied Healthcare Statistics Exam | Q&A
Review Guide | Updated 2026 | Verified
Content
• US Preventative Services Task Force -✓✓develop evidence-based guidelines for
healthcare
• Cochrane Collaboration -✓✓develop and disseminate systemic reviews of healthcare
interventions
• Florence Nightingale David -✓✓produced the first edition of Tables of the Correlation
Coefficient
• population -✓✓The entire group having some characteristic (eg, all people with
depression, all residents of the United States). Often a sample is taken of the population
and then the results are generalized to that population.
• sample -✓✓A group selected from the population in the hope that the smaller group
will be representative of the entire population.
• parameter -✓✓characteristic of a population
• statistic -✓✓characteristic of a sample
• descriptive statistic -✓✓numerical or graphical summaries of data, and may include
charts, graphs, and simple summary statistics such as means and standard deviations
to describe characteristics of a population sample
• inferential statistics -✓✓statistical techniques (e.g., chi-square test, the t test, the one-
way ANOVA) that allow conclusions to be drawn about the relationships found among
different variables in a population sample
• quantitative variables -✓✓a characteristic that can be measured numerically, a
characteristic with numeric values that have meaning and for which arithmetic
operations such as adding and averaging make sense. Ex: height, weight, income,
heart rate, etc.
• categorical variables -✓✓a characteristic that takes on values that are names or
labels.These are data that cannot be averaged or represented by a scatter plot as they
have no numerical meaning. Ex: The color of a ball, gender, year in school.
, • data -✓✓the raw materials of research, they provide the numbers upon which we
perform statistics
• variables -✓✓any characteristic that can and does assume different values for the
different people, objects, or events being studied
• random assignment -✓✓Assignment of individuals to groups by chance (ie, every
subject has an equal chance of being assigned to a particular group).
• independent variable -✓✓The variable that is seen as having an effect on the
dependent variable. In experimental designs, the treatment is manipulated.
• dependent variabe -✓✓The variable that measures the effect of some other variable
• treatment group -✓✓the group in an experimental investigation that is subjected to the
change in the independent variable
• control group -✓✓In an experiment, the group that is not exposed to the treatment;
contrasts with the experimental group and serves as a comparison for evaluating the
effect of the treatment.
• continuous variable -✓✓A variable that can take on any possible value within a range.
For example, weight is a continuous variable because a weight of 152.5 lb makes
sense.
• discrete variable -✓✓number of children is a discrete variable because it can take on
only certain values (0, 1, 2, and so on). A value of 1.2 for children does not make any
conceptual sense.
• experimental methods -✓✓Research methodologies that involve the manipulation of
independent variables in order to determine their effects on the dependent variables.
• correlation methods -✓✓research methods that allow researchers to investigate how
are two variables are related
• nominal scale -✓✓the numbers are simply used as codes representing categories or
characteristics, and there is no order to the categories; they are categorical or
qualitative; usually assigned numbers as codes for computer storage, but they are
arbitrary and do not represent any kind of meaningful order (e.g. gender, ethnicity,
region of residence, marital status)
• ordinal scale -✓✓measured with numbers representing categories that can be placed
in a meaningful numerical order (e.g., from lowest to highest) but for which there is no
information regarding the specific size of the interval between the different values, and
Review Guide | Updated 2026 | Verified
Content
• US Preventative Services Task Force -✓✓develop evidence-based guidelines for
healthcare
• Cochrane Collaboration -✓✓develop and disseminate systemic reviews of healthcare
interventions
• Florence Nightingale David -✓✓produced the first edition of Tables of the Correlation
Coefficient
• population -✓✓The entire group having some characteristic (eg, all people with
depression, all residents of the United States). Often a sample is taken of the population
and then the results are generalized to that population.
• sample -✓✓A group selected from the population in the hope that the smaller group
will be representative of the entire population.
• parameter -✓✓characteristic of a population
• statistic -✓✓characteristic of a sample
• descriptive statistic -✓✓numerical or graphical summaries of data, and may include
charts, graphs, and simple summary statistics such as means and standard deviations
to describe characteristics of a population sample
• inferential statistics -✓✓statistical techniques (e.g., chi-square test, the t test, the one-
way ANOVA) that allow conclusions to be drawn about the relationships found among
different variables in a population sample
• quantitative variables -✓✓a characteristic that can be measured numerically, a
characteristic with numeric values that have meaning and for which arithmetic
operations such as adding and averaging make sense. Ex: height, weight, income,
heart rate, etc.
• categorical variables -✓✓a characteristic that takes on values that are names or
labels.These are data that cannot be averaged or represented by a scatter plot as they
have no numerical meaning. Ex: The color of a ball, gender, year in school.
, • data -✓✓the raw materials of research, they provide the numbers upon which we
perform statistics
• variables -✓✓any characteristic that can and does assume different values for the
different people, objects, or events being studied
• random assignment -✓✓Assignment of individuals to groups by chance (ie, every
subject has an equal chance of being assigned to a particular group).
• independent variable -✓✓The variable that is seen as having an effect on the
dependent variable. In experimental designs, the treatment is manipulated.
• dependent variabe -✓✓The variable that measures the effect of some other variable
• treatment group -✓✓the group in an experimental investigation that is subjected to the
change in the independent variable
• control group -✓✓In an experiment, the group that is not exposed to the treatment;
contrasts with the experimental group and serves as a comparison for evaluating the
effect of the treatment.
• continuous variable -✓✓A variable that can take on any possible value within a range.
For example, weight is a continuous variable because a weight of 152.5 lb makes
sense.
• discrete variable -✓✓number of children is a discrete variable because it can take on
only certain values (0, 1, 2, and so on). A value of 1.2 for children does not make any
conceptual sense.
• experimental methods -✓✓Research methodologies that involve the manipulation of
independent variables in order to determine their effects on the dependent variables.
• correlation methods -✓✓research methods that allow researchers to investigate how
are two variables are related
• nominal scale -✓✓the numbers are simply used as codes representing categories or
characteristics, and there is no order to the categories; they are categorical or
qualitative; usually assigned numbers as codes for computer storage, but they are
arbitrary and do not represent any kind of meaningful order (e.g. gender, ethnicity,
region of residence, marital status)
• ordinal scale -✓✓measured with numbers representing categories that can be placed
in a meaningful numerical order (e.g., from lowest to highest) but for which there is no
information regarding the specific size of the interval between the different values, and
