# data[].attributes.body
Our goal was to perform an experimental design to see if fake incorrect answers (or priming) will influence PV students' answers to a simple, visual question.

# data[].attributes.body
As a group, we wanted to determine whether individuals might conform to the common consensus if the individuals perceive many other students as selecting a wrong answer. As such, we created a multiple choice question where one must match a sample pattern to one of three options (A, B, or C). The patterns were created such that C was the obvious choice. We then created a fake data sheet with many B's written so as to appear as though many students had chosen option B rather than option C. Our group selected a stratified random sample, picking one or two subjects from each table group, of Perkiomen Valley High School students. Of the sample, we gave 40 of the participants the multiple choice question along with the fake data sheet such that they could see the numerous answers of B before them, and 44 of the participants answered the multiple choice question without seeing any previous answers. We then recorded the proportion of individuals who chose the correct answer or didn't conform, A or C, versus the individuals who chose the wrong answer, B. We took the recorded proportions for both groups (the fake data sheet group and the non-fake data sheet group) and ran the appropriate test to determine whether there was a significant difference between the two groups' proportions of who answered correctly/didn't conform.

# data[].attributes.body
The bar on the left side of the graph shows the sample that was primed with a fake data sheet. The bar of the right side shows the sample that was not primed and told to give answers orally. The red is the subjects that chose A or C. In the primed group, these were subjects that did not conform. The green shows the subjects that chose B. These were subjects that conformed for the primed group. The graph shows that more people in the primed group chose B than did the group without priming.

# data[].attributes.body
After running a statistical test, the results proved to be statistically significant, meaning that it is highly unlikely that they were simply caused by chance. The test returned a p-value of .004 which means that in similar sample sizes, there is a 0.4% chance of obtaining results that are as extreme or more extreme than the ones observed. Since the probability of our results being caused by chance is less than 1%, we have sufficient evidence to conclude that the priming had an effect on students by making some of them conform to pick an incorrect answer, B.