Without strong communication skills, all the advanced analysis we have performed might be overrun. At this event, our expert panelists will share tips and advice on how to clearly and effectively communicate statistics, particularly in social media, and answer questions from the audience.
The debate over the value and interpretation of p-value has endured since the time of its inception nearly 100 years ago. The use and interpretation of p-values vary by a host of factors, especially by discipline. These differences have proven to be a barrier when developing and implementing boundary-crossing clinical and translational science. The purpose of this panel discussion is to discuss misconceptions, debates, and alternatives to the p-value.
This talk will focus on leveraging social media to communicate statistical concepts. From summarizing other’s content to promoting your own work, we will discuss best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content.
Clear statistical communication is both an educational and public health priority. This talk will focus on best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. It is increasingly important for people to be able to sift through what is important and what is noise, what is evidence and what is an anecdote. This talk focuses on techniques to strike an appropriate balance, with specifics on how to communicate complex statistical concepts in an engaging manner without sacrificing truth and content.
Data Science, as a broad and interdisciplinary field, is one of the fastest growing areas of student interest (and employment opportunities). The traditional introductory statistics courses that would typically serve as a gateway to data science need modernized curricula and pedagogy in order to adapt to today’s increasingly large and complex data sources and data science questions. In this session, we share our experience to address the following issues: • What constitutes the fundamentals of good data science practice? • How to teach a data science course with innovative pedagogy? • How to improve communication skills to bridge data scientists and practitioners? • How to take advantage of virtual learning? Discussant: Linda Zhao Speakers: Leanna House on Adapting student engagement strategies for a virtual environment, Lucy D’Agostino McGowan on Bringing Data Science Communication into the Classroom, and Nusrat Jahan on Data Science Education in Undergraduate Setting. There will be three speakers and a discussant in this session.
We are thrilled to host a series of experts to discuss their experiences working with different types of COVID-19 data, insights they’ve gleaned, and challenges they’ve encountered with these complex and rapidly evolving data.
In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work.
We are interested in studying best practices for introducing students in statistics or data science to the programming language R. The “tidyverse” is a suite of R packages created to help with common statistics and data science tasks that follow a consistent philosophy. We have created two sets of online learning modules, one that introduces tidyverse concepts first and then dives into idiosyncrasies of R as a programming language, the second that takes a more traditional approach, first introducing R broadly and then following with an introduction to a particular suite of packages, the tidyverse. We have created a randomized study to examine whether the order certain concepts are introduced impacts whether learning objectives are met and/or how engaged students are with the material. This talk will focus on the mechanics of this study: how it was designed, how we enrolled participants, and how we evaluated outcomes.
Clear statistical communication is both an educational and public health priority. This session will focus on best practices for effective statistical communication that simultaneously is clear, engaging, and understandable while remaining rigorous and mathematically correct. The panelists have a range of experience with communicating complex statistical concepts to both technical and lay audiences via multiple communication mechanisms including podcasting, Twitter, engaging with journalists in print, and television correspondence on networks such as CNN and BBC. The session will begin with moderated questions posed by the organizer and then open the discussion to audience members.
This talk will cover two R packages: matahari ( https://github.com/jhudsl/matahari) and tidycode ( https://lucymcgowan.github.io/tidycode/). The matahari package is a simple package for tidy logging of everything you type into the R console. The tidycode package allows users to analyze R expressions in a tidy way (i.e. take the code captured from matahari and put it in a tidy table for downstream analysis with the tidyverse).