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PSYC 2040 Study Guide - Final Guide: Gynaecology


Department
Psychology
Course Code
PSYC 2040
Professor
David Stanley
Study Guide
Final

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Research Statistics Lecture Summaries
Lecture 1: Introduction
- The purpose of statistics is to make a conclusion about a large group of people (a
population) from a small group of people (a sample).
Describing the Population
- Population mean is an average
oIdeally we would like to be able to describe the population using this
population mean
oHowever this is impractical, we cannot survey everyone
oThis is why we use a sample mean to estimate the population mean
o We are usually interested in a sample only to the extent that it tells us
something about the population
- Population standard deviation
oThe larger the SD is, the larger the differences
oIt is used to describe how accurate the mean is in describing everyone in
the population
oIt is “roughly, on average, the amount that a score differs from the
population mean”
- Standard error of the mean
oRoughly, on average, the amount that sample means differ from the
population mean due to the effects of random sampling
oIt is the amount of variability in sample means that we would expect if we
used random sampling
oDescribes the variability
oLarger sample sizes result in small standard errors, that means there is less
variability
- Estimated Standard deviation
oRoughly, on average, the amount that a score differs from the sample
mean
oBased on our single sample, this is our best guess of what the population
standard deviation is
- Estimated standard error of the mean
oRoughly, on average, the amount that a sample means differ from the
population mean
oBased on our single sample, this is our best guess of what the Standard
Error of sample means would be with repeated sampling
oYou can also think of SE based on your sample as our best guess of the
amount of variability in sample means that we would expect if we used
random sampling
oNote that when researchers say “Standard Error, they are usually
referring to “Estimated Standard Error
oRatios are extremely important way of comparing 2 things in statistics

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- We typically based the denominator on standard error (standard error squared) in
these ratios
Central Limit Theorem
- Regardless of the shape of the population distribution, the distribution of sample
means will
o Always be roughly normal
oHave a standard error equal to the population standard deviation divided
by the square root of sample size
Lecture 2: T-tests
1. Single-Sample T-test
- The focus is on 1 population
Communicate Findings
I examined the intelligence quotient (IQ) of University of Guelph graduate students using
a random sample of 200 students. A two-tailed single-sample t-test revealed that the
average IQ of University of Guelph graduate students (M = 120.53, SE = .77) was
significantly higher, t(199) = 26.60, p < .001, than 100. Thus, University of Guelph
graduate students may be smarter than the average person.
- Identify
oWho the participants are
oHow many
oDependent variable (IQ)
oType of test (1 or 2 tail)
oSignificant or non-significant
oHigher/lower
oRandom sampling
op or ns
Hypothesis
Null Hypothesis: H0: μ = 100
oAny difference between the sample mean and the hypothesized population
mean is simply due to random sampling error
oAssuming alpha is .05, p > .05 is non-significant and just due to random
sampling
- Alternative Hypothesis: H1: μ 100
oIf you reject the null hypothesis, you conclude that the alternative
hypothesis is correct
oSpecifically you conclude that the difference between the sample mean
and the hypothesized population mean is so large, that it could not be due
to random sampling
oAssuming alpha is .05, p < .05 is significant and more than just random
sampling o We reject it if the p-value is less than 5% chance
Assumptions
1. Random sampling from a clearly defined population
2. Population values are normally distributed

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- P-value indicates the probability that you would obtain a t-value as extreme as you did
when the population mean is the hypothesized one
2. Independent-Groups T-test
- The focus is on 1 independent variable with 2 levels/groups and no repeated measures
Communicate findings
I examined the extent to which University (University of Waterloo, University of Guelph)
influenced ratings of movie enjoyment using a two-tailed independent-groups t-test. The
homogeneity of variance assumptions was violated, Levenes F(1, 98) = 5.21, p < .05,
therefore I used the t-test formula based on separate variance estimates (rather than
pooled). University of Guelph students (M = 82.98, SE = .44) rated the movie
significantly higher t(89) = 2.45, p <.05, Cohens d = .49, than University of Waterloo
students (M = 84.80, SE = .60). Moreover, this difference can be considered medium,
using Cohens standards. Thus, University of Guelph students enjoyed the movie more
than University of Waterloo Students
- Identify
oIndependent/dependent variables
oIndicate the name of the test
oReport the mean and standard error
oReport tests of assumptions
oReport primary test results and effect sizes
oUse the word “significant and “non-significant where appropriate
oUse ns, p < .05, p < .01, p < .001 as needed
oConcluding sentence for a general audience
Hypothesis
- Null Hypothesis: H0: m
Guelph = m
Waterloo
oThe assumption is that the mean of Group 1 is the same as the mean of
Group 2
oTherefore, any differences between the groups means is due to random
sampling error
oAssuming alpha is .05, p > .05 is non-significant and just due to random
sampling
- Alternative Hypothesis: H1: m
Guelph m
Waterloo.
oIf you reject the null hypothesis, you conclude that the alternative
hypothesis is correct
oSpecifically you conclude that the sample means are so different that it
could not be due to random sampling
oAssuming alpha is .05, p < .05 is significant and more than just random
sampling o We reject it if the p-value is less than 5% chance
Assumptions
1. Random sampling from 2 clearly defined populations
2. Population values are normally distributed
3. Variances of the 2 populations are the same, known as the homogeneity of
variance
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