psychology 2030 section n dr. matthew tata wednesdays 6pm to 9pm
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Psychology 2030
• Section N
• Dr. Matthew Tata
• Wednesdays 6pm to 9pm
Text
• Thinking with Data by John R. Vokey and Scott W. Allen
• Supplementary material will be put on reserve in the library
Wallowing in it• There are no assignments to be turned in
• But you still have to work on stats regularly
• You will be assigned to “starter” groups which you can modify at will
• Meet once per week to work through practice questions and help each other think through whatever you don’t understand
Evaluation• Mid-term 45%
• Final 45%
• Project 10%
Midterm and final are WebCT but do not have to be taken in the testing centre
Each test can be re-taken once!
Project
• Ask a question and find out the answer.– The question will be one for which the answer involves
some comparison of measurements (and should be interesting to you)
– Some examples:• “Does the price of gasoline correlate with the price of crude oil?”
• “Which tastes better: coke or pepsi”?
– You will have to make some field observations and/or conduct an experiment
– Due on the last day of class
GradesYour final Letter grade will be calculated as follows:Percentage Letter Grade90-100 A+85-89 A80-84 A-77-79 B+73-76 B70-72 B-67-69 C+63-66 C60-62 C-55-59 D+50-54 D<50 F
Interaction• Office hours: after class or by
appointment
• Email: [email protected]– Rules: – 1. No emailing the night before a test– 2. Yes-or-no questions only!
What is this course about?
What is this course about?• There is variability in everyone and
everything– That’s great news, but what if we want to
compare two things– What if we want to compare two groups of
things
What is this course about?• Relationships between sets of events or
things– For example: the correlation between
smoking and lung cancer
What is this course about?• Relationships between sets of events or
things– For example: the correlation between
smoking and lung cancer
– But what about the correlation between cigarette lighter use and lung cancer?
What is this course about?• Thinking critically about data
– 98% of all drowning victims were not wearing lifejackets.
What is this course about?• Thinking critically about data
– 98% of all drowning victims were not wearing lifejackets.
– Does this mean that lifejackets save lives?
What is this course about?• Thinking critically about data
– 98% of all drowning victims were not wearing tuxedos
– Does this mean that tuxedos save lives?
What is this course about?• Out of 100 boating accidents:
Survived Drowned
Lifejacket 49 1
No Lifejacket 1 49
What is this course about?• Out of 100 boating accidents:
• Graphs depict data but take some savvy to interpret
0
10
20
30
40
50
60
Survived Drowned
LifejacketNo Lifejacket
What is this course about?• What if the data came out this way?:
Survived Drowned
Lifejacket 1 1
No Lifejacket
49 49
What is this course about?• Out of 100 boating accidents:
• Now you might conclude that there is no effect of lifejackets on survival
0
10
20
30
40
50
60
Survived Drowned
LifejacketNo Lifejacket
What is this course about?• Likelihood of a particular event or
scenario happening by chance
– Would you pick 1, 2, 3, 4, 5, 6 for lottery numbers?
What is this course about?• Likelihood of a particular event or
scenario happening by chance
– Would you pick 1, 2, 3, 4, 5, 6 for lottery numbers?
– How about 1,2,3,24,35,22 ?
What is this course about?• Likelihood of a particular event or
scenario happening by chance
– Would you pick 1, 2, 3, 4, 5, 6 for lottery numbers?
– How about 1,2,3,24,35,22 ?– How about 9,27,12,3,36,18 ?
What is this course about?• Likelihood of a particular event or
scenario happening by chance
Each number is an independent event so the odds are 1 in 13,983,816 regardless of the numbers you pick!
What is this course about?• Are two things the same or different?
How do you find out?
What is this course about?• Are two things the same or different?
How do you find out?
• eg. two coins
What is this course about?• Are two things the same or different?
How do you find out?
• eg. two coins
• measure them somehow
What is this course about?• Are two things the same or different?
How do you find out?
• Problem:– if you measure something and measure it
again, you will get different numbers
What is this course about?• Are two things the same or different?
How do you find out?
• Problem:– if you measure something and measure it
again, you will get different numbers– if you measure something and measure
something else, you will get different numbers
What is this course about?• Are two things the same or different?
How do you find out?
• Problem:– How different do two things have to be to
be “officially” different?
What is this course about?• Are two things the same or different?
How do you find out?
• Answer:– Different enough so that there is only a 1 in
20 chance (p = .05) that you concluded that they are different when they really are the same
Start Observing the Data Around You
• If you hear what you think to be an interesting statistic or (especially) a misrepresentation of data, write it down or cut it out and bring it in!
Next Lecture:• Distributions, the mean, and the median
(chapter 2)
• Measures of Variability (Chapter 3)
Group 1
Aitkens, Erin L. (Erin)Akins, Lindsay M. (Lindsay)Ambeskovic, Mirela (Mirela)Andrusiak, Jessica M. (Jessica)Beattie, Stephanie L. (Stephanie)Bergen, Kristi D. (Kristi)Berrow, Jennifer K. (Jennifer)Bodnar, Jillian R. (Jill)
Group 2
Brookes, Samantha J. (Sam)Churko, Andrea D. (Andrea)Derksen, Stacy L. (Stacy)Desrochers, Melissa A. (Melissa)DiFruscia, Emilia (Emily)Digout, Lewanna N. (Lewanna)Ducharme, Danielle N. (Danielle)Epp, Saralee M. (Saralee)
Group 3
Feyter, Janelle J. (Janelle)Fisher, Erinn N. (Erinn)Gasteiger, Sabrina G. (Sabrina)Gebrenigus, Essey A. (Essey)Gerein, Quintina R. (Quincy)Geschwendt, Brittany D. (Brittany)Gould, Lori A. (Lori)Gregorash, Susan B. (Susan)
Group 4
Hall, Kyleen A. (Kyleen)Heigl, Melanie T. (Melanie)Hendrickson, Megan L. (Megan)Jensen, Devonee A. (Devonee)Johnson, Marisa (Marisa)Kleinjan, Evelien H. (Linda)Kloos, Nicole M. (Nicole)Kluk, Dawn N. (Dawn)
Group 5
Leigh, Jennifer M. (Jennifer)MacLachlan, Ian K. (Ian)Mason, Aja L. (Aja)Melting Tallow, Derek (Derek)Merkeley, Carley A. (Carley)Middleton-Hope, Christopher J. (Chris)Millen, Tannia S. (Tannia)Moland, Mark S. (Mark)
Group 6
Morris, Michelle J. (Michelle)Murray, Karly H. (Karly)Nieboer, Chelsea R. (Chelsea)Nieboer, Jodie M. (Jodie)Norton, Tara H. (Tara)Okayasu, Nozomi (Nozomi)Peru, Chantel R. (Chantel)Pinches, Ariane D. (Ariane)
Group 10
Velji, Alisha (Alisha)Walsh, Kimberly D. (Kim)Wells, Keri L. (Keri)Wennekers, Charmaine J. (Charmaine)Wyght, Valena-Rose E. (Valena)Xiong, Yi Mei (May)Zhu, Yu (Yu)
Group 7
Polsfut, Brandie L. (Brandie)Proudfoot, Timothy E. (Timothy)Qu, Tieying (Tina)Quintin, Dominique L. (Dominique)Raynard, David L. ()Rechenmacher, Holly R. (Holly)Rossler, Vanessa R. (Vanessa)Sanderson, Lindsay E. (Lindsay)
Group 8
Sarson, Scott D. (Scott)Sipchenko, Jessica L. (Jessica)Slingerland, Raelene M. (Raelene)Spence, Vanessa K. (Vanessa)Staehr, Richelle M. (Richelle)Tams, Reuben J. (Reuben)te Raa, Leah M. (Leah)Teierle, Denise R. (Denise)
Group 9
Thai, Phuong T. (Fiona)Tooley, Brandy J. (Brandy)Tornquist, Kendra R. (Kendra)Uy, Narath (Narath)Van Maanen, Alisa G. (Alisa)Van Uden, Amanda E. (Amanda)Vanden Brink, Shannon Y. (Shannon)Vasic, Kristina (Kristina)
• Meet up and exchange emails or set a meeting time