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University of Toronto St. George

Sociology

SOC202H1

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Winter

Description

Key Terms:
• Statistical inference: the process for making statements about the broader population
based on the use of sample data from that population
• Sample statistic:A numeric value in the sample
• Population Parameter:A numeric value in the population
• Generalize:Applying information from the sample to the population
• Sampling error: sometimes the sample mean will be more or less than the population
mean
• Point of estimate: statistic that is used to estimate the parameter; for example the sample
mean
• Unbiased estimator:Astatistic’s value equals the value of the parameter being measured
• Sampling distribution:Atheoretical distribution of all the potential values of a sample
statistic that you would find if you could draw all possible combinations of a random
sample
• The Mean of the Sampling Distribution Means: the average of all of the means in the
sample distribution
• Standard Error of the Mean: the standard deviation of the sampling means
• Null Hypothesis: States that the result was due to chance and as such there is no true
difference or relationship
• Alternative hypothesis: The result was not due to chance and represents a real difference
or relationship
• One-tailed Hypothesis: When a hypothesis states a specific direction
• Two-tailed Hypothesis: Hypothesis does not have a specific direction of the difference,
relationship, or effect
• Significance Level: the value used to determine whether to reject or fail to reject the null
hypothesis
• Confidence Intervals:Arange of values that we expect will contain the true population
parameter • Level of Confidence: the estimated probability that the true value of the population
parameter falls within the stated confidence interval
• Estimated standard error of the mean: The estimate of the standard error of the mean
based on sample deviations
• Degrees of freedom: The number of independent observations used in estimating a
parameter
• Independent Samples: Samples that consist of different individuals and the selection of
the first group does not influence the selection of the second group
• Dependent samples: Samples that consist of the same individuals, and/or the selection of
the first group determines the selection of the second
• Assumption of homogeneity of variances:Assumption that the variances are equal across
the groups under investigation
• Pooled sample Variance:An unbiased estimator of the variance that both groups have in
common
• Factor: the independent variable inANOVAtesting
• Grand Mean: is the total of all observations divided by the total sample size
• Between Group Variance: the total amount that the group means vary from the grand
mean
• Within Group variance: the total amount that all individual observations vary from their
group mean
• F-Ratio: the ratio of the between group variance to within group variance
• Between group sum of squares: the sum of the squared deviations of each group mean
from the grand mean
• Within group sum of squares: the sum of the squared deviations of each observation from
its group mean
• Total sum of squares: the sum of each of the squared deviations of each observation from
the grand mean
• Mean sum of squares for between groups: estimated variance for the between groups • Mean sum of squares for within groups: estimated variance for within groups
• Contingency Tables: table that compares two or more categorical variables by cross
tabulating the categories of each variable
• The phi-coefficient: appropriate for measuring the strength between two variables in a
two by two contingency table
Key Concepts
Chapter 6: Sample to the Population
• Results must be representative of the population in order to generalize
• The sample must have the same characteristics or attributes as those in the population
• Four common ways of probability sampling:
o Simple random sampling: select potential respondents randomly, but everyone has
a chance
o Systematic random sampling: randomly determining the first individual then
every nth
o Stratified sampling: takes into account different parts of the structure of a
population. Makes subgroups based on traits then uses simple random sampling
for each group
o Cluster sampling: Same as stratified however its uses homogeneous groups then
does simple random sampling of each of these groups
• Sampling error is the difference between the population mean and the sample mean
• Sample is mean is a point of estimate
• To decrease the sampling error, increase the sample size
o This will include more information about the population
• Central Limit Theorem Statement #1:
o The mean of the sampling distribution means equals the population mean
o Sample mean equals true population mean for two reasons: Although sample mean can be either slightly less or more, it has an equal
chance of going either way
Larger samples will have sample means that will be closer to the
population mean
• Sample size usually greater than 30
• Central Limit Theorem Statement #2:

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