American Statistical Association
Statistical Collaboration Training for Applied Statisticians
Training students in the art of effective statistical collaboration is essential for their applied statistics education. Challenging situations can arise in collaborations that are necessary to discuss before they arise. Julia Sharp, Emily Griffith, and Megan Higgs, with help from Ann Hess, received support from the American Statistical Association Strategic Grant Initiative program to create a set of 10 short videos illustrating challenging and salient topics of communication for training in statistical collaboration. Scenarios portrayed in the videos convey realistic statistical collaboration encounters.
The 10 videos, with accompanying scripts and discussion questions, are intended for initiating meaningful discussion and reflection. The written scripts can be used for reference during a discussion or to facilitate student role-playing. Two discussion questions are given at the end of each video, with additional questions provided in the accompanying materials.
A brief description of each of the 10 videos, along with their length, is provided below. The link for each topic will take you to the materials for each video.
Scenario 1 (3.50 minutes)
Countering stereotypical views of statisticians
Keywords: statistician stereotype; project planning; study design; ANCOVA;
Scientific collaborators may initially communicate as if a statistician is a technician, using phrases such as “I just need you to run the statistics” or “I just need you to calculate a sample size.” In this video, a researcher makes such a statement and the statistician responds. The scenario encourages discussion about next steps and how the conversation could have been handled differently, while also providing an opportunity to discuss the importance of asking specific questions about study design.
Scenario 2 (4.08 minutes)
Defining the scope of the project/collaboration
Keywords: roles; expectations; repeated measurements; funding expectations; thesis or dissertation; graduate student research
For a statistician, negotiating one’s scope of work and role in a collaboration can lead to difficult conversations. This video presents a scenario portraying specific challenges that may arise when the project is the basis of a graduate student’s thesis or dissertation.
Scenario 3 (2.34 minutes)
Turning down requests for new work from a current collaborator
Keywords: understanding statistician workload capacity; funding; statistical support; declining requests; establishing boundaries; workload;
A statistician may need or want to decline requests for new or additional work from a current collaborator for many reasons, including a lack of time or even negative experiences with the collaborator. This video portrays a discussion between a collaborator who requests assistance with new projects and a statistician who turns down the work. The scenario encourages discussion about prioritizing projects and establishing boundaries.
Scenario 4 (4.03 minutes)
Discussion about a realistic timeline for work
Keywords: understanding statistician workload capacity; timeline for analysis; data management; exploratory data analysis; descriptive analysis; co-authorship; workload;
Collaborators may believe a statistician should be able to accomplish proposed work in an unrealistically short time frame. This video depicts a scenario where a researcher expresses an opinion that the statistician’s part of the work should not take much time, and the statistician responds. The video promotes discussion about negotiations, timelines, and expectations.
Scenario 5 (3.48 minutes)
Beginning and ending a meeting effectively
Keywords: meeting structure; initial meeting; ethics; enforcing professional boundaries;
An effective beginning and ending are important to leading a productive meeting. This video depicts a scenario where the collaborator’s expectations for an initial meeting do not align with those of the statistician, and where the statistician finds it difficult to end the meeting on time. This scenario can be used to start a discussion about running an effective meeting and establishing expectations, as well as the ASA’s Ethical Guidelines for Statistical Practice.
Scenario 6 (4.27 minutes)
Using statistical results to identify questions of interest
Keywords: secondary data analysis; funding; identifying objectives; ethics
Statistical analysis may be used in an exploratory way to identify specific comparisons or questions for which to focus a report or future research. In this video, a collaborator requests that the statistician assist with “finding what’s significant and important” to drive the current research findings. This video encourages discussion about assuming good intentions, the importance of distinguishing between a priori and post hoc comparisons (or exploratory and confirmatory analysis), and the ASA’s Ethical Guidelines for Statistical Practice.
Scenario 7 (4.18 minutes)
Statistically significant vs. practically meaningful
Keywords: sample size; statistical significance; practically meaningful effect; clinically significant;
A researcher’s need to justify a sample size for a study often provides motivation for contacting a statistician. The process of carrying out a sample size investigation is usually more involved than expected by the researcher (who might think of it as a simple mathematical calculation). This video depicts one of the most challenging parts of the study planning process -- discussing the difference between statistical significance and practical importance.
Scenario 8 (3.13 minutes)
Advocating for data visualization
Keywords: data visualization; data anomalies; exploratory data analysis; study design
Introductory statistical training typically does not emphasize the importance of creatively plotting data before summarizing and aggregating. Statisticians may be in a position to advocate for and assist with effective visualizations for a given project, particularly when the researcher may not have the necessary computing expertise. In this video, a researcher and statistician discuss data visualization as a first step in the analysis, and identify important design information that would have otherwise been overlooked. The scenario encourages discussion about the importance of thorough exploratory data analysis and considering data visualization as an integral part of the analysis process.
Scenario 9 (4.07 minutes)
Navigating requests related to p-hacking and survey validation
Keywords: p-hacking; survey validation; clarifying research objectives; statistical significance; ethics
Understanding and reflecting on professional ethics related to being a statistician is an important aspect of statistical collaboration. This video presents a meeting where a scientist describes their plan to use statistical significance to decide what to report in a publication, putting the statistician in an ethical dilemma. The video also motivates discussion about issues related to pressure put on researchers to use “validated” instruments and obtain “significant” results.
Scenario 10 (5.44 minutes)
Pseudoreplication and refusal to use a statistician's advice
Keywords: pseudoreplication; publication with atypical analyses; discipline norms; co-authorship; ethics; acknowledgement for contributions; establishing professional boundaries;
Statisticians may contribute work that is reasonable and sound, only to ultimately have a collaborator ignore the contributions. This may be because the methods used do not align with researcher perceptions of norms in their discipline, or due to outside pressure from advisors, reviewers, funding agencies, or others. This video portrays a statistician in such a situation and elicits discussion of ethical questions, such as how a statistician should be acknowledged for work, particularly if they disagree with the approach ultimately used. The scenario can also lead to more technical discussions about pseudoreplication.