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The Influence of Suggestion on Subjective Preferences

The Influence of Suggestion on Subjective Preferences. By Sean Oh, Joshua Marcuse, and David Atterbury Math 5: Chance. Goal. Hypothesis: Social conformity is an essential trait of human nature. Subjective preferences may be susceptible to suggestion when this trait is activated.

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The Influence of Suggestion on Subjective Preferences

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  1. The Influence of Suggestion on Subjective Preferences By Sean Oh, Joshua Marcuse, and David Atterbury Math 5: Chance

  2. Goal • Hypothesis: Social conformity is an essential trait of human nature. • Subjective preferences may be susceptible to suggestion when this trait is activated. • The goal of the experiment was to show that subjective preferences can be swayed by suggestion.

  3. Experimental Design • Two Pictures of female models: A and B. • Three Treatments: A, B, and Control. • In each treatment we ask males to state which model they think is more beautiful. • However, in Treatments A and B we tried to influence the respondent’s preference with a suggestion to see if it affected his answer.

  4. Picture A

  5. Picture B

  6. Null Hypothesis Respondents in Treatment A and Treatment B are equally likely to prefer model A or model B as they did in Treatment Control.

  7. Alternate Hypothesis Respondents in Treatment A will tend to prefer model A and respondents in Treatment B will tend to prefer model B compared to the Treatment Control.

  8. How did we try to influence them? Our script for Treatments A and B said: “Hello. I am conducting a psychology experiment. Would you please look at these two pictures. In our recent study, a majority of people stated that the woman in Picture A [or B] is more beautiful. Do agree or disagree that the woman in Picture A [or B] is more beautiful?”

  9. Treatment Control • For the Control we tried to establish a baseline against which we could compare the results of Treatments A and B. • We did not make any suggestion to attempt to influence the respondent. • We hoped to get as close to 50% as possible for Pictures A and B.

  10. Treatment Control Script Our script for Treatment Control said: “Hello. I am conducting a psychology experiment. Would you please look at these two pictures and tell me if you think the woman in Picture A is more beautiful, or do you think the woman in Picture B is more beautiful?”

  11. How we collected the data • We interviewed 135 people for the experiment. • Each treatment contained 45 respondents. • All respondents were RANDOMLY selected. • We collected data in Thayer, Collis and Novack, during the morning, afternoon, and evening. • 15 respondents were interviewed at each location. Then we aggregated the data so all three Treatments included data taken from all three locations during all three times of day. • We used three interviewers to administer the question from the script. • Each respondent was interviewed separately, and additional precautions were taken to avoid any external influence on the respondent during the experiment.

  12. Why we excluded women • We wanted to include men and women in our study, but… • When we asked women in pre-test whether they preferred Model A or Model B, we got a surprising result…

  13. Massive bias among females

  14. The Math…

  15. Parameter • Treatment Control gauged the parameter of preference for model A and model B. • N = 45 • Preference for model A = 24/45 = .533 • Preference for model B = 21/45 = .467

  16. Significance Level and Critical Region • Significance Level = 3.67% • Critical Region for Treatment A: • PA ≥ 30 people • Critical Region for Treatment B: • PB ≥ 27 people

  17. What does that mean? If 30 or more respondents choose model A in Treatment A and if 27 or more respondents choose model B in Treatment B, we can say with over 95% certainty that subjective preferences were influenced by our comments.

  18. Power • We chose .7 as a power. We believed that 70% of respondents would choose model A in Treatment A and 70% of respondents would choose model B in Treatment B. • Using this power, we found that there would be a 31.21% chance of a Type II error in Treatment A and a 7.21% chance of a Type II error in Treatment B.

  19. What does this mean? • According to our power, if 70% of people truly preferred model A in Treatment A, we have about a 31% chance of not reaching the critical value and thus incorrectly concluding that respondents were not influenced. • Same for model B in Treatment B, except this is only a 7% chance.

  20. Results • Treatment A: • Preference for model A: 33/45 = .733 • Preference for model B: 12/45 = .267 • Treatment B: • Preference for model A: 30/45 = .667 • Preference for model B: 15/45 = .333

  21. Preferences for Model A and B

  22. Analysis of Results • Our results were certainly surprising. • Just looking at the numbers, we can say with 96% certainty that people were influenced in Treatment A AND we can say with 93% certainty that people were NOT influenced in Treatment B. • In conclusion, the data does not support our hypothesis at all.

  23. Possible explanations • Sample size was too small to indicate the subtlety of our hypothesis. • Treatment Control misrepresented the population. People actually preferred model A to model B at a 2:1 ratio, but we only got a 1:1 ratio by chance.

  24. More Possible Explanations • Bias in test administration • Suggestion influenced the respondents, but not in the way we predicted

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