The student will be able to:

1. Distinguish among different scales of measurement and their implications
2. Identify the standard methods of obtaining data and identify advantages and disadvantages of different sampling techniques and techniques for obtaining data
3. Interpret data displayed in tables and graphs
4. Determine measures of central tendency and variability for a given data set
5. Calculate measures of central tendency and variability for discrete distributions
6. Apply concepts of sample space and probability
7. Calculate probabilities using normal and t-distributions
8. Explain the difference between sample and population distributions and the role played by central limit theorem
9. Calculate z-scores
10. Construct standardized distributions from z-scores
11. Construct and interpret confidence intervals
12. Explain the basic concept of hypothesis testing, including Type I and II errors
13. Interpret levels of statistical significance, including p-values
14. Compute by hand and interpret data from many types of statistical tests, including the z-test, the single-sample t-test, the independent samples t-test, the repeated measures t-test, one-way ANOVA, pearson's correlation, spearman's correlation, linear regression, and chi-square
15. Compute using software, such as SPSS, Excel, or R, and interpret data from many types of statistical tests, including the z-test, the single-sample t-test, the independent samples t-test, the repeated measures t-test, one-way ANOVA, pearson's correlation, spearman's correlation, linear regression, and chi-square
16. Interpret the output of a computer-based statistical analysis from programs, such as SPSS, Excel, or R
17. Formulate a hypothesis test (e.g., choose the forms of null and alternative hypotheses) involving samples from two populations
18. Use appropriate statistical techniques to analyze and interpret applications based on data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
19. Apply simple regression analysis for estimation, inference, and interpret the associated statistics
20. Calculate and interpret measures of effect size for many different types of statistical tests, including but not limited to single-sample t-test, independent samples t-test, repeated measures t-test, one-way ANOVA, pearson's r, and chi-square

1. Introduction to statistics
   1. Explain the definitions of statistics, science, and observation
   2. Populations and samples
   3. Sampling methods
   4. Advantages and disadvantages of sampling methods, including but not limited to random sampling, cluster sampling, stratified sampling, and convenience sampling
   5. The scientific method and the design of research studies
   6. Variables and measurement
   7. Scales of measurement as they relate to variables associated with behavioral and social sciences, such as psychology, sociology, economics
   8. Statistical notation
   9. Overview of how statistical operations are applied to research within business, social sciences, psychology, life science, health science, and education
2. Frequency distributions
   1. Frequency distribution tables
   2. Frequency distribution graphs
   3. The shape of frequency distributions
   4. Sample spaces
3. Central tendency
   1. The mean
   2. The median
   3. The mode
   4. Selecting a measure of central tendency
   5. Measures of relative position
   6. Binomial distributions
   7. Random variables
   8. Discrete distributions
   9. Explanation for how to interpret means and "mean differences" that are reported in behavioral science literature
   10. Central tendency and the shape of the distribution
   11. Interpretations of behavioral aspects of data sets with different shapes and skews
   12. How to report measures of central tendency in the literature using APA format
   13. Applications of measures of central tendency using data sets from business, social sciences, psychology, life science, health science, and education
4. Variability
   1. The range and the interquartile range
   2. Standard deviation and variance for a population
   3. Standard deviations and variance for samples
   4. How to determine if a sample is biased or unbiased using variance
   5. How to interpret the behavioral aspects of data sets with relatively small, medium, and large standard deviations
   6. How to interpret the behavioral aspects of sample data sets by analyzing the relationship between the mean and standard deviation
   7. How to report the standard deviation in the literature using APA format
   8. Applications of measures of variability using data sets from business, social sciences, psychology, life science, health science, and education
5. Correlation and regression
   1. The pearson correlation
   2. Using and interpreting the pearson correlation
   3. Uses and applications of the pearson correlation to research in the behavioral sciences, including study design, inter-rater reliability, and concurrent validity and construct validity
   4. Hypothesis tests and the pearson correlation
   5. Reporting correlations in the literature using APA format
   6. The point-biserial correlation and measuring effect size with r-squared
   7. The spearman correlation
   8. Applications of pearson's r correlation and the spearman correlation using data sets from business, social sciences, psychology, life science, health science, and education
   9. Introduction to regression
   10. Analysis and interpretation of data using statistical software, such as SPSS, Excel, or R
   11. Applications of regression using data sets from business, social sciences, psychology, life science, health science, and education
6. Z-scores: location of scores and standardized distributions
   1. Definition and introduction to z-scores
   2. Z-scores and location in a distribution
   3. Using z-scores to standardize a distribution
   4. Other standardized distributions based on z-scores
   5. The connection between z-scores and inferential statistics
   6. Explanation of how inferential statistics are used in the behavioral sciences, such as business, psychology, life sciences, sociology, economics, and education
7. Probability
   1. Explanation of the concept of probability using data sets that are exemplar to behavioral sciences
   2. Probability and the normal distribution
   3. Probabilities and proportions for scores in a normal distribution
   4. The connection between probability and inferential statistics
   5. Binomial and discrete distributions
   6. Random variables and expected value
8. Probability and samples: the distribution of sample means
   1. Samples and sampling error
   2. The distribution of sample means
   3. Probability and the distribution of sample means
   4. The standard error of the mean
   5. How to report the standard error in the literature using APA format
   6. The connection between the distribution of sample means and inferential statistics
   7. Binomial distributions
9. Introduction to hypothesis testing
   1. The logic of hypothesis testing within the behavioral sciences
   2. Uncertainty and errors in hypothesis testing
   3. Null and alternative hypothesis
   4. P-value and criteria for a decision of significance
   5. Z-test as an introduction to hypothesis testing
   6. Type I and Type II errors
   7. Single-sample z-test
   8. Comparison of how one-tailed and two-tailed tests are used within social and behavioral science research, including in disciplines such as business, psychology, sociology, economics, and education
   9. Measuring effect size with Cohen's d
   10. Interpretations of Cohen's d
   11. Importance of effect size and reporting effect size in the literature using APA format
   12. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
10. Single sample t-test
    1. Hypothesis tests with a single-sample t-statistic (a t-test with one population)
    2. Applications of the single-samples t-statistics to research design in the behavioral sciences
    3. Measuring effect size for the single-sample t-statistic (Cohen's d and r-squared)
    4. Reporting the results of a t-test in the literature using APA format
    5. Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
    6. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    7. Technology based statistical analysis, such as SPSS, Excel, or R
11. The independent samples t-test (between subjects t-test)
    1. Research design within the behavioral sciences for an independent-samples t-test (a t-test with two populations)
    2. Hypothesis tests and effect size with the independent-measures t-statistic
    3. Reporting the results of an independent-measures t-test in the literature using APA format
    4. Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
    5. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    6. Technology based statistical analysis, such as SPSS, Excel, or R
12. The repeated measures t-test (within subjects t-test)
    1. Research design within the behavioral sciences for a repeated measures t-test (within subjects t-test), which is a t-test with two populations
    2. Hypothesis tests and effect size for the repeated-measures t-test
    3. Reporting the results of a repeated-measures t-test in the literature using APA format
    4. Analysis and interpretation of data using hand calculations and statistical software, such as SPSS, Excel, or R
    5. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    6. Technology based statistical analysis, such as SPSS, Excel, or R
13. Estimation and confidence intervals
    1. Overview of estimation
    2. How to calculate and utilize confidence intervals
    3. Interpretation of confidence intervals using data sets that are exemplar to behavioral sciences
    4. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    5. Technology based statistical analysis, such as SPSS, Excel, or R
14. Introduction to ANOVA
    1. The logic of analysis of variance
    2. ANOVA notation and formulas
    3. Applications of one-way ANOVA to behavioral science research
    4. The distribution of F-ratios
    5. Examples of hypothesis testing and effect size with ANOVA
    6. Post-hoc tests
    7. Reporting the results of analysis of variance in the literature
    8. Analysis and interpretation of data using hand calculations and statistical software using APA format
    9. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    10. Technology based statistical analysis, such as SPSS, Excel, or R
15. Two-factor analysis of variance
    1. Introduction to two-factor analysis of variance and how it is used within the behavioral sciences
    2. Introduction to main effects and interactions
    3. Interpretations of main effects and interactions
    4. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    5. Technology based statistical analysis, such as SPSS, Excel, or R
16. Chi-square test
    1. Introduction to the non-parametric statistics
    2. Calculating the chi-square test by hand and also using statistical software, such as SPSS, Excel, or R
    3. Calculating a chi-square test with no preference
    4. Calculating a chi-square test from a known population
    5. Single factor and multiple factor (e.g., 2x2) chi-square tests
    6. Interpreting the results of chi-square tests
    7. Effect size measures of chi-square
    8. Assumptions and limitations of chi-square
    9. Reporting of chi-square tests using APA-style format
    10. Applications using data from at least two of the following disciplines: business, economics, political science, administration of justice, life science, physical science, health science, information technology, and education
    11. Technology based statistical analysis, such as SPSS, Excel, or R

Not applicable.

Multiple choice quizzes
Hand computations
Weekly homework assignments
Data entry and analysis and interpretation of results using SPSS
Research papers
Summaries and analysis of primary source research articles
Problem-solving exercises
Midterm exams
Final exams

Lectures
In-class group and individual activities
Class discussion
Active learning exercises

Gravetter, Frederick Jr., and Larry B. Wallnau. Essentials of Statistics for the Behavioral Sciences, 9th ed.. 2018.

Sarty, Gordon E.. Introduction to Applied Statistics for Psychology Students. 2020.

Sarty text available as OER: https://openpress.usask.ca/introtoappliedstatsforpsych/

1. Reading assignments:
   1. Reading and studying of textbook
   2. Reading and critically analyzing primary source research articles
2. Writing assignments:
   1. Weekly homework assignments
   2. Interpretations to data analysis
   3. Computing statistical operations on data