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SOC202H1 (21)
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# Test review 2.docx

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School
University of Toronto St. George
Department
Sociology
Course
SOC202H1
Professor
all
Semester
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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