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This course presents fundamental statistical concepts and tools for understanding and analyzing data from studies in the social and behavioral sciences. Topics include measures of central tendency and dispersion, probability theory, data distributions, significance testing, and statistical inference. Students will learn how to analyze and interpret data from psychological studies using descriptive statistics, correlational methods, t tests, and analysis of variance procedures.
Identify the differences between sample and population data.
Discriminate between descriptive and inferential statistics.
Describe and interpret psychological data using descriptive statistics.
Define reliability and validity.
Interpret data quality using measures of reliability and validity.
Explain the role of probability in statistical analysis using the standard normal distribution.
Explain the logic of hypothesis testing.
Differentiate between the null and research hypothesis.
Describe significance testing and the meaning of probability levels.
Differentiate between Type I and Type II error.
Evaluate group differences using a two-sample independent t test.
Describe the advantages of using an ANOVA over the use of multiple t tests.
Evaluate group differences using a one-way ANOVA.
Interpret the IBM® SPSS® output of a one-way ANOVA.
Explain the difference between correlation and causation.
Describe relationships among variables using the Pearson product-moment correlation coefficient.
Describe statistical significance in reference to the correlation coefficient.
Describe the assumptions of t tests and ANOVA.
Discriminate between parametric and nonparametric tests.
Identify when to use parametric versus nonparametric tests.
Describe the comparisons in a chi-square analysis.