Professor Lora Connor

Purpose for the Course

• Useful for understanding statistical reporting in academic journals and in the media.
• Recommended or required for many undergraduate and graduate psychology programs.
• The course is designed to be taught for transfer credit, degree credit, and career preparation and/or advancement.

What will I learn?

• Define important statistical terms, use mathematical notation, and describe relationships among concepts.
• Differentiate descriptive from inferential statistics.
• Distinguish among different scales of measurement and identify the attributes and implications of each.
• Interpret data displayed in tables and graphs.
• Calculate measures of central tendency and variation for a given data set.
• Calculate the mean and variance of a discrete distribution.
• Describe central tendency, use measures to explain research data, and appropriately graph results.
• Apply concepts of sample space and probability.
• Identify the standard methods of obtaining data and identify advantages and disadvantages of each.
• Find and interpret relative position of values in a distribution.
• Distinguish between sample and population distributions and analyze the role played by the Central Limit Theorem.
• Explain sampling, compute probability, apply methods to solve problems, and interpret the position of values in the normal curve distribution.
• Calculate probabilities using normal and Student’s t-distributions.
• Explain hypothesis testing, including: stating null and alternative hypotheses, determining decision rules using alpha levels, assessing the likelihood of Type I and II errors, computing test statistics, and evaluating the test statistic in comparison to the critical values.
• Formulate hypothesis tests involving samples from one and two populations.
• Determine and interpret levels of statistical significance including p-values.
• Select the appropriate technique for testing a hypothesis and interpret the result.
• Describe, differentiate, compute, and interpret various statistical tests, including: z Tests, t Tests, F Tests, and nonparametric tests.
• Compute and interpret effect size using Cohen's d, r-squared, and eta-squared.
• Construct and interpret confidence intervals.
• Perform parameter estimation - both point estimates and confidence intervals - for values such as μ, μ1-μ2, and μD.
• Discuss correlational research, identify strengths and weaknesses, explain relationships, perform associated computations, and interpret results.
• Use linear regression and ANOVA analysis for estimation and inference, and interpret the associated statistics.
• Acquire skills for organizing data, performing statistical analyses, interpreting results, and making conclusions.
• Perform statistical analysis using technology such as SPSS, Excel, or graphing calculators.
• Interpret the output of a technology-based statistical analysis.
• Use appropriate statistical techniques to analyze and interpret applications based on data from disciplines including psychology, social sciences, business, life science, health science, and education.