# Chapter 1: The Language of Statistics

Answers for all ‘Test Yourself’ questions from the book to check your performance and widen your overall understanding of the contents.

1.
1. Quasi-experiment.  The stores are being assigned from two unique populations: west city and east city stores.  There is no random assignment.
2. No. A quasi-experiment cannot be used to provide evidence of causality.  To answer the question he wants to answer, she could change this into an experiment by randomly assigning stores to conditions.
3. The addition of free extra cheese to some stores is the treatment.  No change is the control.
4. Hypothesis: Adding double cheese free of charge to pizza orders will increase customer satisfaction scores.
5. Sally is drawing her participants from a sample of her customers.
6. The construct for the addition of free extra cheese is not clear from this description.  Doing this could be increasing customer perceptions of value, improving the flavor of the pizza, increasing the appearance of the pizza, or any number of other explanations.  The other is easier: customer satisfaction is the construct, and the Likert-type scale is its operationalization.
7. Addition of cheese is qualitative, while the Likert-type scale is quantitative.
8. Both are discrete.
9. The addition of free extra cheese is nominal (yes or no), but an argument could be made that it is ordinal (more cheese versus no cheese). The Likert-type scale is interval.
2.
1. Ratio
2. Nominal
3. Ordinal
4. Interval
5. Ratio
6. Nominal
3. There are many possible answers here.  Perhaps the most obvious is this: most online social media platforms have some way to track how many people “like” or “follow” your product or organisation.  We might consider the number of followers (a count) to be an appropriate operationalization – more followers equals greater success.  We would consider this operational definition quantitative, discrete, and ratio measurement (but not all operational definitions that you might come up with would share these properties).