Undergraduate Statistics (K300) Videos and Resources
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Abstract
Many students think of K300 as a math course. While it is true that we will perform some calculations along the way, this course is not about the math. Instead, this course will focus on the underlying logic and principles of statistical analysis so that you understand what the numbers tell you (and what they don't tell you), not just how to generate them.
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Introductory College Students (General Education)
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PSY-K300: Statistical Techniques
Teaches:
Select and calculate appropriate descriptive statistics and make visual representations of data.
Demonstrate an understanding of the importance of sampling randomness and measurement noise in statistical inference.
Understand population parameters and how to estimate them.
Pick an appropriate statistical technique to test a hypothesis about a particular treatment or experiment.
Explain and interpret p values with respect to the null and alternative hypotheses.
Interpret and manipulate basic statistical notations and formulas including summation notation and formulas for both descriptive and inferential statistics. Specific examples will include formulas for the mean, variance, and standard deviation; calculation and interpretation of z scores and understanding of the standard normal distribution; and ability to conduct and interpret the results of t-tests.
Perform a variety of statistical analyses either by hand or with the appropriate software tools.
Discuss a set of results including p values, confidence intervals, and effect sizes, with respect to real world relevance and suggested next steps.
Identify and critique examples of good and bad statistical reasoning in the popular press.
Identify problems with classical statistical techniques and demonstrate an awareness of alternate methodologies including Bayesian ideas.
Demonstrate an understanding of the importance of sampling randomness and measurement noise in statistical inference.
Understand population parameters and how to estimate them.
Pick an appropriate statistical technique to test a hypothesis about a particular treatment or experiment.
Explain and interpret p values with respect to the null and alternative hypotheses.
Interpret and manipulate basic statistical notations and formulas including summation notation and formulas for both descriptive and inferential statistics. Specific examples will include formulas for the mean, variance, and standard deviation; calculation and interpretation of z scores and understanding of the standard normal distribution; and ability to conduct and interpret the results of t-tests.
Perform a variety of statistical analyses either by hand or with the appropriate software tools.
Discuss a set of results including p values, confidence intervals, and effect sizes, with respect to real world relevance and suggested next steps.
Identify and critique examples of good and bad statistical reasoning in the popular press.
Identify problems with classical statistical techniques and demonstrate an awareness of alternate methodologies including Bayesian ideas.
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