Expect to spend 10 hours working on this class.
The goal of this course is to improve your ability to thoroughly read and critically appraise the methods sections of scientific literature. Our focus will be on understanding when each technique is appropriate and interpreting written methods and results in scientific literature. For each technique, you will gain an understanding of when to use a given approach, what assumptions it has, and the interpretation of the associated statistics. Across 5 modules, you will be systematically introduced to the most common statistical techniques, along with articles that use those techniques so we can see the techniques in action in the wild. No previous experience with statistics or coding is required or expected.
The class is made up of 5 lessons. Lessons include a video lecture, a knowledge check, an assignment and a comprehension check. A syllabus is posted in the class detailing class requirements.
All assignments must be completed in order to qualify for continuing education credit.
By registering for this class, you are agreeing to the NNLM Code of Conduct
- Summarize the differences between various descriptive statistics and describe when it is appropriate to use each.
- Develop understanding of statistical inference, why we need to use a sample to learn about a population, and how we infer from a sample to a population.
- Define a p-value, describe its drawbacks, and list some alternatives.
- Identify scenarios where each statistical test is appropriate (t-test, ANOVA, linear regression, chi-square, logistic regression, survival analysis, Poisson regression).
- Describe the assumptions of each statistical test (t-test, ANOVA, linear regression, chi-square, logistic regression, survival analysis, Poisson regression).
- List common alternatives used when assumptions are not met.