Chapter 7: Problems

7.1 Match each term with its definition.

1. alternative hypothesis	a. In design, making a visualization easy to interpret and understand
2. categorical data	b. Approach to examining data that seeks to explore the data says without testing formal models or hypotheses
3. classification analyses	c. Design rule suggesting that a viz should not contain too much or too little, but just the right amount of data
4. confirmatory data analysis	d. Avoiding the intentional or unintentional use of deceptive practices that can alter the users understanding of the data being presented
5. data deception	e. Intentional arranging of visualization items in a way to produce emphasis
6. data ordering	f. 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 group
7. data overfitting	g. Any visual representation of data, for example graphs, diagrams, or animations
8. effect size	h. Subset of data used to train a model for future prediction
9. emphasis	i. Quantitative measure of the magnitude of the effect
10. ethical presentation	j. 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 message
11. exploratory data analysis	k. Data items that take on a limited number of assigned values to represent different groups
12. extrapolation beyond the range	l. Subset of data not used for the development of a model but used to test how well the model predicts the target outcome
13. machine learning	m. Process of estimating a value beyond the range of data used to create the model
14. null hypothesis	n. When a model is designed to fit training data very well but does not predict well when applied to other datasets
15. outlier	o. In design, the amount of attention that an element attracts
16. simplification	p. Testing a hypothesis and providing statistical evidence of the likelihood that the evidence refutes or supports a hypothesis
17. test dataset	q. In design, making it easy to know what is most important
18. training dataset	r. Data point, or a few data points, that lie an abnormal distance from other values in the data
19. type I error	s. Incorrect rejection of a true null hypothesis
20. type II error	t. Techniques that identify various groups and then try to classify a new observation into one of those groups
21. visual weight	u. Application of artificial intelligence that allows computer systems to improve and to update prediction models without explicit programming
22. visualization	v. 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