In this repository, we share course materials for two of our course sessions in the R3 Center for Innovation in Science Education (R3ISE, https://www.jhsph.edu/departments/w-harry-feinstone-department-of-molecular-microbiology-and-immunology/academics-and-degree-programs/R3-PhD-program/index.html) housed at the Johns Hopkins Bloomberg School of Public Health (BSPH). The respective sessions are "Data Science & Visualization in the Biomedical and Life Sciences" and "Hypothesis Testing Framework and Caveats of p-values". (For more information about the program, visit the website above and for our philosophy, please access https://www.nature.com/articles/d41586-018-01853-1). We provide direct screenshots of the course page as it is visible to students on the CoursePlus BSPH platform, along with uploaded videos from VoiceThread of the lecture material, and lastly, the references and online resources that we use for the respective sessions.
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Objectives
- Define the relationship between data science and the scientific method
- Employ basic vocabulary in the field of quantitative reasoning
- Describe the importance of data visualization as a component of data storytelling
- Identify elements that comprise effective vs. poor data visualization
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Discussion Thread - Data Visualization Exercise:
- After going through the presentation and required reading for this session, think about data visualization broadly
- Please provide an example of what you consider either effective or poor data visualization from your own line of work, articles, the media, etc. (post the graph from the source into your response)
- Briefly explain what the graph displays or what it should convey. What is the statistical test that was used for the analysis - would you say this was correctly chosen?
- What would you change in the visualization?
- Your primary post and reactions to at least 2 classmates' posts are due by [date]
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Files
- R3easoning/Session - Data Science & Visualization in the Biomedical and Life Sciences.png --> screenshot of course page
- R3easoning/Video - Data Science & Visualization in the Biomedical and Life Sciences.mov --> video of course lecture
- R3easoning/Online Library Materials - Data Science & Visualization in the Biomedical and Life Sciences.docx --> references and resources
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Objectives
- Explain the basic conceptual framework of hypothesis testing
- Describe how to interpret the results of hypothesis testing
- Explain the significance of p-values and caveats when using them
- Describe some of the pitfalls of statistical significance and how these elements may tie in with rigor in science
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Discussion Thread - The hypothesis testing framework, statistical significance, and caveats of p-values: Part A
- Think of a brief experiment or study you would like to conduct (or one you've already done), either in the lab or by doing any other data analysis that has come across or is interested in. If you have trouble figuring out a suitable example, please contact us and we will help.
- Briefly describe that study, experiment, or other data analysis in 2-3 sentences.
- What would your hypotheses be? (include null and alternative)
- What type of data and measurements would you collect? Would you be working with means/proportions/other values?
- For now, don't focus so much on the type of test statistic you would apply. Let's suppose the p-value you generate is 0.04. What does this mean?
- Formulate a statement about statistical significance with regard to your proposed hypotheses.
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Discussion Thread - The hypothesis testing framework, statistical significance, and caveats of p-values: Part B
- After going through the presentation, interview, and required reading for this session, reflect on hypothesis testing and p-values.
- Please provide an example from a peer-reviewed article in your research field of interest that utilized p-values.
- What was the conclusion reached by the authors based on the statistical significance analysis?
- Did the authors use other metrics besides p-values to strengthen their conclusions?
- What are your views on p-values and their role in hypothesis testing?
- Your primary post and reactions to at least 2 classmates' posts are due by [date]
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Files
- R3easoning/Session - Hypothesis Testing Framework and Caveats of p-values.png --> screenshot of course page
- R3easoning/Video - Hypothesis Testing Framework and Caveats of p-values.mov --> video of course lecture
- R3easoning/Online Library Materials - Hypothesis Testing Framework and Caveats of p-values.docx --> references and resources