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SOCY 211 (69)
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Lecture 4

# SOCY211 Week 4, Lecture 2

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School
Queen's University
Department
Sociology
Course
SOCY 211
Professor
Carl Keane
Semester
Winter

Description
Z-Scores and the Normal Curve - Using examples from the SOCY211 test 1 - Mean: 30/40= 75% Standard Deviation (s)= 5 - 68% of the class will fall within one standard deviation below the mean of 30 and one standard deviation above the mean of 30 (between 25-35 out of 40—62.5%-87.5%) - We can compute Z scores: i.e., 15 students scored 32/40= 80% - Z score for 32= ̅ - From z score to the mean= 0.1554 Area beyond the z score= 0.3446 - Calculation of the percentage of people that they did better than (people with a score of 32)= 0.1554 + 0.5 (other side of the mean)= 0.655 = 65% - Score that is above the mean has a positive z score; below the mean, negative z score - Example: 25/40= 62.5% - Area to the mean- 0.3413 Area beyond z score- 0.1587 - Students that scored 25/40 did better than 15.9% of the class - If we add the area between their z scores and the mean (0.3413) to the area above the mean (0.5) we get 0.8413; therefore 84% of the class scored better than students who scored 25/40 Inferential Statistics - Refers to a process whereby researchers are able to make inferences about a population based on results from the sample o Ex. Public opinion polls do this - Instead of asking the population we draw a sample (subset) of people from the population - If we want to learn the population parameters our sample needs to be representative of the population from which the sample is drawn - Most basic technique that is used: simple random sampling o It’s like a lottery—random selection - Everyone has an equal chance of being selected - This is achieved through the EPSEM (Equal probability of selection method) - Allows us to assume that our sample is representative of the larger population and then we can generalize our results from the sample to the population - We are interested in how are our results reflective - There are three concepts: o 1. Sampling distribution o 2. Central limit theorem o 3. Confidence intervals - We are
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