Summary
Picking the right statistical test is only the beginning of a data analysis project. In fact, formal statistical tests are a relatively minor part of a well-done data analysis. An effective data analysis should also include cleaning and checking your data for errors, documenting steps, and checking for robustness, to name just a few steps. Unfortunately, these skills are often neglected in formal statistical training—applied researchers are rarely taught how to analyze data from start to finish.
In this “how to” webinar, Associate Professor Kristin Sainani will walk you through the steps of a complete data analysis, using real data on mental health in athletes. She will give practical, hands-on tips for how to approach each step of the analysis and how to improve rigor and reproducibility of your research.
- What steps are needed for an effective data analysis
- The importance of cleaning, checking, understanding, and plotting data
- How to improve the rigor and reproducibility of your research
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