WGU D772 Statistical Data Literacy Mastery
Vocabulary Guide
Data
The actual values of the variables. Data is the raw information that we collect and analyze to gain
insights into the phenomenon we are studying.
Statistic
A statistic is a numerical value that describes a characteristic of a sample. It is calculated from the data
collected from the sample and used to estimate the corresponding population parameter. Since we
cannot measure the entire population, statistics are essential for making inferences and generalizations
about the broader group.
Variables
The characteristics or measurements that we are interested in studying and the specific aspects of the
individuals that we collect data on. Variables can be either quantitative (numerical) or categorical
(qualitative).
Quantitative Variables
These are numerical measurements, such as age, height, weight, or income.
Categorical Variables
These are categories or labels, such as gender, race, occupation, or favorite color.
Population
The entire group of individuals or objects that you want to study.
Example: All high school students in the United States.
Sample
A smaller, more manageable group selected from the population to represent the larger group.
Example: A group of 500 high school students from different states across the U.S.
, Individuals
the objects described by a set of data. These can be people, animals, or things. In essence, they are the
entities on which we collect information.
Parameter
A parameter is a numerical value describing an entire population's characteristics. It is a fixed value, but
it is often unknown because collecting data from every member of a large population is usually
impractical or impossible.
Numerical Values
The characteristics of populations and samples. These values are known as parameters and statistics.
Understanding the difference between these two terms is essential for interpreting statistical results
and drawing meaningful conclusions from data.
Stratified Sampling
Dividing the population into distinct subgroups called strata, based on specific characteristics such as
age, gender, or socioeconomic status. Once the population is divided into strata, a random sample is
taken from each stratum in proportion to its representation in the overall population. This ensures that
the sample reflects the diversity of the population in terms of the chosen characteristics.
Strata
Subgroups within a population that share a common characteristic (e.g., age group, gender, income
level).
Proportionate Sample
A sample where the number of individuals selected from each stratum is proportional to the size of that
stratum in the population.
Cluster Sampling
Dividing the population into clusters, which are naturally occurring groups like schools, neighborhoods,
or cities. Instead of randomly selecting individuals from the entire population, researchers randomly
select a few clusters and include all individuals within those selected clusters in the sample. This method
is often used when it's difficult or expensive to sample individuals directly from the entire population.
Cluster
A naturally occurring group within a population (e.g., school, neighborhood, city).
Systematic Sampling
Selecting every "nth" individual from a list of the population, starting from a randomly chosen point.
This method is often used when it's easy to access a list of the entire population.
Sampling Interval
The fixed distance between the selected elements in a systematic sample.
Vocabulary Guide
Data
The actual values of the variables. Data is the raw information that we collect and analyze to gain
insights into the phenomenon we are studying.
Statistic
A statistic is a numerical value that describes a characteristic of a sample. It is calculated from the data
collected from the sample and used to estimate the corresponding population parameter. Since we
cannot measure the entire population, statistics are essential for making inferences and generalizations
about the broader group.
Variables
The characteristics or measurements that we are interested in studying and the specific aspects of the
individuals that we collect data on. Variables can be either quantitative (numerical) or categorical
(qualitative).
Quantitative Variables
These are numerical measurements, such as age, height, weight, or income.
Categorical Variables
These are categories or labels, such as gender, race, occupation, or favorite color.
Population
The entire group of individuals or objects that you want to study.
Example: All high school students in the United States.
Sample
A smaller, more manageable group selected from the population to represent the larger group.
Example: A group of 500 high school students from different states across the U.S.
, Individuals
the objects described by a set of data. These can be people, animals, or things. In essence, they are the
entities on which we collect information.
Parameter
A parameter is a numerical value describing an entire population's characteristics. It is a fixed value, but
it is often unknown because collecting data from every member of a large population is usually
impractical or impossible.
Numerical Values
The characteristics of populations and samples. These values are known as parameters and statistics.
Understanding the difference between these two terms is essential for interpreting statistical results
and drawing meaningful conclusions from data.
Stratified Sampling
Dividing the population into distinct subgroups called strata, based on specific characteristics such as
age, gender, or socioeconomic status. Once the population is divided into strata, a random sample is
taken from each stratum in proportion to its representation in the overall population. This ensures that
the sample reflects the diversity of the population in terms of the chosen characteristics.
Strata
Subgroups within a population that share a common characteristic (e.g., age group, gender, income
level).
Proportionate Sample
A sample where the number of individuals selected from each stratum is proportional to the size of that
stratum in the population.
Cluster Sampling
Dividing the population into clusters, which are naturally occurring groups like schools, neighborhoods,
or cities. Instead of randomly selecting individuals from the entire population, researchers randomly
select a few clusters and include all individuals within those selected clusters in the sample. This method
is often used when it's difficult or expensive to sample individuals directly from the entire population.
Cluster
A naturally occurring group within a population (e.g., school, neighborhood, city).
Systematic Sampling
Selecting every "nth" individual from a list of the population, starting from a randomly chosen point.
This method is often used when it's easy to access a list of the entire population.
Sampling Interval
The fixed distance between the selected elements in a systematic sample.
