Exploration 1.3 Do People Use Facial Prototyping? 17
c01_Tintle2e_Exploration 1.3_1PP No
te:To ensure full functionality, including saving text in input fields and adding images in image fields, please download and use Student Name:
Exploration 1.3 Do People Use Facial Prototyping?
A study in Psychonomic Bulletin and Review (Lea, Thomas, Lamkin, and Bell, 2007) presented evidence that “people use facial prototypes when they encounter different names.” Participants were given two faces and asked to identify which one was Tim and which one was Bob. The researchers wrote that their participants “overwhelmingly agreed” on which face belonged to Tim and which face belonged to Bob but did not provide the exact results of their study. STEP 1: Ask a research question. We will gather data from your class to investigate the research question of whether students have a tendency to associate certain facial features with a name. STEP 2: Design a study and collect data. Each student in your class will be shown the same two pictures of men’s faces used in the research study. You will be asked to assign the name Bob to one photo and the name Tim to the other. Each student will then submit the name that he or she assigned to the picture on the left. Then the name that the researchers identify with the face on the left will be revealed. 1. Identify the observational units in this study. 2. Identify the variable. Is the variable categorical or quantitative?
The parameter of interest here is the probability that a student in your class would assign the same name to the face on the left. 3. State the null and alternative hypotheses to be tested when the data are collected. Express these both in words and symbols. (Hint: Think about the parameter and the research question of interest here.)
Consider these two photos:
Melissa A. Lea/Robin D. Thomas/Nathan A. Lamkin/Aaron Bell ©Psychonomic
Bulletin & Review 2007, 14 (5), 901–907.Melissa A. Lea/Robin D. Thomas/Nathan A. Lamkin/Aaron Bell ©Psychonomic Bulletin & Review 2007, 14 (5), 901–907.
c01Exploration 1.3.indd 17Adobe Acrobat Reader (free) or any Adobe Acrobat DC product. 18 CHAPTER 1 Significance: How Strong Is the Evidence? c01_Tintle2e_Exploration 1.3_1PP 4. Do you think the face on the left is Bob or Tim? STEP 3: Explore the data. 5. Collect the responses (data) for all the students in y our class. How many students put Tim as the name on the left? How many students participated in this study (sample size)? What pr
oportion put Tim’s name on the left?
When we conduct analyses with binary variables, we often call one of the outcomes a “success” and the other a “failure” and then focus the analysis on the “success” outcome. It is arbitrary which outcome is defined to be a success, but you need to make sure you do so con-sistently throughout the analysis. In this case we’ll call “Tim on left” a success because that’s what previous studies have found to be a popular choice.
STEP 4: Draw inferences. You will use the One Proportion applet to investigate how surprising the observed class statistic would be if students were just randomly selecting which name to put with which face. 6. Before y
ou use the applet, indicate what you will enter for the following values:
a. Probability of success:
b. Sample size:
c. Number of r
epetitions: 7. Conduct this simulation analysis. Make sur
e the Proportion of heads button is selected in the applet and not Number of heads
.
a. Indicate how t
o calculate the approximate p-value (count the number of simulated statistics that equal ____ or ___________). b. Report the approximate p-value.
c. Use the p-
value to evaluate the strength of evidence provided by the sample data against the null hypothesis, in fav
or of the alternative that students really do tend to assign the name Tim (as the researchers predicted) to the face on the left.
The p-value is the most common way to evaluate strength of evidence against the null hypothesis, but now we will explore a common alternative way to evaluate strength of evi-dence. The goal of any measure of strength of evidence is to use a number to assess whether the observed statistic falls in the tail of the null distribution (and is therefore surprising when the null hypothesis is true) or among the typical values we see when the null hypoth-esis is true.
c01Exploration 1.3.indd 18 9/9/20 3:47 PM
c01_Tintle2e_Exploration 1.3_1PP No
te:To ensure full functionality, including saving text in input fields and adding images in image fields, please download and use Student Name:
Exploration 1.3 Do People Use Facial Prototyping?
A study in Psychonomic Bulletin and Review (Lea, Thomas, Lamkin, and Bell, 2007) presented evidence that “people use facial prototypes when they encounter different names.” Participants were given two faces and asked to identify which one was Tim and which one was Bob. The researchers wrote that their participants “overwhelmingly agreed” on which face belonged to Tim and which face belonged to Bob but did not provide the exact results of their study. STEP 1: Ask a research question. We will gather data from your class to investigate the research question of whether students have a tendency to associate certain facial features with a name. STEP 2: Design a study and collect data. Each student in your class will be shown the same two pictures of men’s faces used in the research study. You will be asked to assign the name Bob to one photo and the name Tim to the other. Each student will then submit the name that he or she assigned to the picture on the left. Then the name that the researchers identify with the face on the left will be revealed. 1. Identify the observational units in this study. 2. Identify the variable. Is the variable categorical or quantitative?
The parameter of interest here is the probability that a student in your class would assign the same name to the face on the left. 3. State the null and alternative hypotheses to be tested when the data are collected. Express these both in words and symbols. (Hint: Think about the parameter and the research question of interest here.)
Consider these two photos:
Melissa A. Lea/Robin D. Thomas/Nathan A. Lamkin/Aaron Bell ©Psychonomic
Bulletin & Review 2007, 14 (5), 901–907.Melissa A. Lea/Robin D. Thomas/Nathan A. Lamkin/Aaron Bell ©Psychonomic Bulletin & Review 2007, 14 (5), 901–907.
c01Exploration 1.3.indd 17Adobe Acrobat Reader (free) or any Adobe Acrobat DC product. 18 CHAPTER 1 Significance: How Strong Is the Evidence? c01_Tintle2e_Exploration 1.3_1PP 4. Do you think the face on the left is Bob or Tim? STEP 3: Explore the data. 5. Collect the responses (data) for all the students in y our class. How many students put Tim as the name on the left? How many students participated in this study (sample size)? What pr
oportion put Tim’s name on the left?
When we conduct analyses with binary variables, we often call one of the outcomes a “success” and the other a “failure” and then focus the analysis on the “success” outcome. It is arbitrary which outcome is defined to be a success, but you need to make sure you do so con-sistently throughout the analysis. In this case we’ll call “Tim on left” a success because that’s what previous studies have found to be a popular choice.
STEP 4: Draw inferences. You will use the One Proportion applet to investigate how surprising the observed class statistic would be if students were just randomly selecting which name to put with which face. 6. Before y
ou use the applet, indicate what you will enter for the following values:
a. Probability of success:
b. Sample size:
c. Number of r
epetitions: 7. Conduct this simulation analysis. Make sur
e the Proportion of heads button is selected in the applet and not Number of heads
.
a. Indicate how t
o calculate the approximate p-value (count the number of simulated statistics that equal ____ or ___________). b. Report the approximate p-value.
c. Use the p-
value to evaluate the strength of evidence provided by the sample data against the null hypothesis, in fav
or of the alternative that students really do tend to assign the name Tim (as the researchers predicted) to the face on the left.
The p-value is the most common way to evaluate strength of evidence against the null hypothesis, but now we will explore a common alternative way to evaluate strength of evi-dence. The goal of any measure of strength of evidence is to use a number to assess whether the observed statistic falls in the tail of the null distribution (and is therefore surprising when the null hypothesis is true) or among the typical values we see when the null hypoth-esis is true.
c01Exploration 1.3.indd 18 9/9/20 3:47 PM
