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At the conclusion of the course the successful student will be able to:

1. Define, describe and distinguish between descriptive and inferential statistics, and identify in what contexts each is appropriate.
2. Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key descriptive statistical terms, such as: empirical distribution, frequency distribution, histogram, percentile, quartile, measures of central tendency (median, mode, mean), sum of squares, measures of variance (range, variance, standard deviation, within-groups variance, between-groups variance), standard score/z-scores, covariance, Pearson r, regression coefficient, model, and effect size (e.g., Cohen’s d, Eta squared). 
3. Define, describe, distinguish between, and demonstrate ability to calculate, by hand and/or using statistical software, various key research designs and inferential statistical terms, such as: scales of variables (nominal, ordinal, interval, ratio),  independent variable (IV), dependent variable (DV), theoretical distribution, population, sample, statistic, random sampling, estimator, estimate, probability distribution, parameter, normal distribution, t distribution, F-distribution, Chi-square distribution, sampling distribution, null hypothesis significance testing (NHST), null hypothesis, alternative hypothesis, p-value, alpha, beta, power, type 1 error, type 2 error, critical value, statistical significance, and confidence interval.
4. Describe and explain the logic of inferential statistics. This includes being able to explain what a p-value is, what statistical significance means, and how various factors, such as sample size, effect size, alpha and violation of assumptions, influence the p-value and statistical significance.
5. Calculate, interpret, explain the rationale for, and analyze the assumptions for appropriate test statistics and p-values for situations such as the following: a) there is one IV that has two or more levels and the DV is a scale variable – the IV may be a between or within subjects IV, b) there may be a linear relationship between two variables and the null hypothesis is that the population relationship is 0, c) there are observed frequencies for one or two variables and the null hypothesis is that the distribution of observed frequencies is non-proportional.
6. Interpret basic research results as published in academic journals.