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Oct09 Wk3   Dwb[1]
 

Oct09 Wk3 Dwb[1]

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Driving While Black. Statistics of Minorities being stopped by police.

Driving While Black. Statistics of Minorities being stopped by police.

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    Oct09 Wk3   Dwb[1] Oct09 Wk3 Dwb[1] Presentation Transcript

    • A mathematical investigation into the world Adapted from Thinking Mathematically by Eric Gutstein
      • What is racial profiling?
      • Have you ever experienced racial profiling?
      • Do you think that racial profiling is present in Maryland?
      • We are going to compare:
        • The percentages of people pulled over by police
        • The percentages of people that live in Maryland
      • In Maryland
        • 57.7% of people are White
        • 29.4% of people are Black
        • 6.7% of people are Latino
        • 5.1% of people are Asian
        • 2.1% of people report Other
      • We will simulate the percentage of drivers of each race that would get pulled over if things followed those demographics
      • To do this:
        • Put your worksheet on the floor, grid side up
        • Crumple up a piece of paper into a small ball
        • Drop the ball 20 times on the paper and record where it lands
        • The squares are labeled (W=White, B=Black, L=Latino, A=Asian, O=Other)
        • Count up how many times it landed in each type of square
        • Repeat this process for another 20, recording it seperately
      • 1/20 = 5%
      • 2/20 = 10%
      • 3/20 = 15%
      • 4/20 = 20%
      • 5/20 = 25%
      • 6/20 = 30%
      • 7/20 = 40%
      • 6/20 = 30%
      • 7/20 = 35%
      • 8/20 = 40%
      • 9/20 = 45%
      • 10/20 = 50%
      • 11/20 = 55%
      • 12/20 = 60%
      • We will now calculate the percent of drivers of each race that were “pulled over” in your simulation. Calculate one percent for each group of 20
      • 13/20 = 65%
      • 14/20 = 70%
      • 15/20 = 75%
      • 16/20 = 80%
      • 17/20 = 85%
      • 18/20 = 90%
      • 19/20 = 95%
      • 20/20 = 100%
      • On the front side of your worksheet, there is a number line that is numbered from 0 to 100 for each race.
      • Graph the percentages from each person in your advisory’s simulation on each number line using a line plot
        • (Hint, put an x over the space on the number line where the number would be. If two numbers are the same, stack the x’s on top of each other like this)
      • The x’s on the graphs show what our class represented by chance.
      • These are the approximate percentages of people who were actually pulled over in Maryland in 2008 by race.
        • 50% were White
        • 38% were Black
        • 5% were Hispanic
        • 4% were coded as Other
        • 2% were Asian
      • Draw a circle on each graph to represent the actual percentage.
      • Where do the actual numbers fall in relation to the points on our graph that represent “chance?”
        • Hint: where is the circle in the relation to all the x’s? Is it higher? Is it lower? What does that mean?
      • Are our numbers different enough from what really happened to say that there was racial profiling going on?
      • A study was done in Maryland to see if the police were practicing racial profiling when pulling over drivers.
      • Their conclusions were that there were significant differences between the actual makeup of the state and the amount of people of color that were being stopped.
      • They found that there was not enough evidence to say that racial profiling was the cause of these differences
      • This quote was taken from their conclusion:
        • “ According to the general population of licensed drivers in Maryland, the data suggest that law enforcement officers stopped drivers who were races other than Caucasian more often than they stopped Caucasian drivers. The apparent trend, however, lacks validity because statewide measures of driving behavior that correlate with ethnicity are unavailable for use as denominators. The use of licensed driver data introduces unknown estimation problems because variables such as rates of car ownership, driving behavior, and law enforcement deployment may differ across sub- populations.”
      • What do you think about their conclusions?
      • How can math help you prove your point?
      • The complete report can be found at this website:
        • http://www.mdle.net/traffic/07menu.htm