Chapter 7: Problems
7.1 Match each term with its definition.
1. alternative hypothesisa. In design, making a visualization easy to interpret and understand2.
categorical datab. Approach to examining data that seeks to explore the data says without testing
formal models or hypotheses3. classification analysesc. Design rule suggesting that a viz should
not contain too much or too little, but just the right amount of data4. confirmatory data
analysisd. Avoiding the intentional or unintentional use of deceptive practices that can alter the
users understanding of the data being presented5. data deceptione. Intentional arranging of
visualization items in a way to produce emphasis6. data orderingf. Proposed explanation worded
in the form of an inequality, meaning that one of the two concepts, ideas, or groups will be
greater or less than the other concept, idea, or group7. data overfittingg. Any visual
representation of data, for example graphs, diagrams, or animations8. effect sizeh. Subset of
data used to train a model for future prediction9. emphasisi. Quantitative measure of the
magnitude of the effect10. ethical presentationj. Graphical depiction of information, designed
with or without an intent to deceive, that may create a belief about the message and/or its
components, which varies from the actual message11. exploratory data analysisk. Data items
that take on a limited number of assigned values to represent different groups12. extrapolation
beyond the rangel. Subset of data not used for the development of a model but used to test how
well the model predicts the target outcome13. machine learningm. Process of estimating a value
beyond the range of data used to create the model14. null hypothesisn. When a model is
designed to fit training data very well but does not predict well when applied to other datasets15.
outliero. In design, the amount of attention that an element attracts16. simplificationp. Testing a
hypothesis and providing statistical evidence of the likelihood that the evidence refutes or
supports a hypothesis17. test datasetq. In design, making it easy to know what is most
important18. training datasetr. Data point, or a few data points, that lie an abnormal distance
from other values in the data19. type I errors. Incorrect rejection of a true null hypothesis20.
type II errort. Techniques that identify various groups and then try to classify a new observation
into one of those groups21. visual weightu. Application of artificial intelligence that allows
computer systems to improve and to update prediction models without explicit programming22.
visualizationv. Proposed explanation worded in the form of an equality, meaning that one of the
two concepts, ideas, or groups will be no different than the other concept, idea, or group
w. Failure to reject a false null hypothesis
x. Concept that data analysis is of no value if the underlying data is not of high quality
y. Data dispersion around the central value.
Chapter 7 Problems7.1 Match each term with its definition.1. .pdf
1. Chapter 7: Problems
7.1 Match each term with its definition.
1. alternative hypothesisa. In design, making a visualization easy to interpret and understand2.
categorical datab. Approach to examining data that seeks to explore the data says without testing
formal models or hypotheses3. classification analysesc. Design rule suggesting that a viz should
not contain too much or too little, but just the right amount of data4. confirmatory data
analysisd. Avoiding the intentional or unintentional use of deceptive practices that can alter the
users understanding of the data being presented5. data deceptione. Intentional arranging of
visualization items in a way to produce emphasis6. data orderingf. Proposed explanation worded
in the form of an inequality, meaning that one of the two concepts, ideas, or groups will be
greater or less than the other concept, idea, or group7. data overfittingg. Any visual
representation of data, for example graphs, diagrams, or animations8. effect sizeh. Subset of
data used to train a model for future prediction9. emphasisi. Quantitative measure of the
magnitude of the effect10. ethical presentationj. Graphical depiction of information, designed
with or without an intent to deceive, that may create a belief about the message and/or its
components, which varies from the actual message11. exploratory data analysisk. Data items
that take on a limited number of assigned values to represent different groups12. extrapolation
beyond the rangel. Subset of data not used for the development of a model but used to test how
well the model predicts the target outcome13. machine learningm. Process of estimating a value
beyond the range of data used to create the model14. null hypothesisn. When a model is
designed to fit training data very well but does not predict well when applied to other datasets15.
outliero. In design, the amount of attention that an element attracts16. simplificationp. Testing a
hypothesis and providing statistical evidence of the likelihood that the evidence refutes or
supports a hypothesis17. test datasetq. In design, making it easy to know what is most
important18. training datasetr. Data point, or a few data points, that lie an abnormal distance
from other values in the data19. type I errors. Incorrect rejection of a true null hypothesis20.
type II errort. Techniques that identify various groups and then try to classify a new observation
into one of those groups21. visual weightu. Application of artificial intelligence that allows
computer systems to improve and to update prediction models without explicit programming22.
visualizationv. Proposed explanation worded in the form of an equality, meaning that one of the
two concepts, ideas, or groups will be no different than the other concept, idea, or group
w. Failure to reject a false null hypothesis
2. x. Concept that data analysis is of no value if the underlying data is not of high quality
y. Data dispersion around the central value
