Integrating design thinking in the data analytic process

As biostatisticians, we are often tasked with collaborating on a data analysis with many stakeholders. While much has been written about statistical thinking when designing these analyses, a complementary form of thinking that appears in the practice of data analysis is design thinking – the problem-solving process to understand the people for whom a product is being designed. For a given problem, there can be significant or subtle differences in how a biostatistician (or producer of a data analysis) constructs, creates, or designs a data analysis, including differences in the choice of methods, tooling, and workflow. These choices can affect the data analysis products themselves and the experience of the consumer of the data analysis. Therefore, the role of a producer can be thought of as designing the data analysis with a set of design principles. This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between producers of data analyses. We then provide a mathematical framework for alignment between the data analysts and their audience. This will hopefully provide guidance for future work in characterizing the data analytic process.

Analytic Design Theory: Framework for Alignment Between Analyst and Audience

This talk will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We will then introduce a mathematical framework for describing the alignment in these principles between the analyst and audience.

Moving Towards Best Practice: Dispelling Statistical Myths

This talk will focus on best practices for using modern statistics in health sciences.

Data Jamboree: Analyzing NYC 311 Service Requests in R

A data jamboree is a party of different computing tools solving the same data science problems. The NYC Open Data of 311 Service Requests contains all 311 requests of NYC from 2010 to present. This talk with demonstrate how to analyze this data using R.

Design Principles of Data Analysis

The data revolution has sparked greater interest in data analysis practices. While much attention has been given to statistical thinking, another type of complementary thinking that appears in data analysis is design thinking – a problem-solving approach focused on understanding the intended users of a product. When facing a problem, differences arise in how data analysts construct data analyses, including choices in methods, tools, and workflows. These choices impact the analysis outputs and user experience. Therefore, a data analyst’s role can be seen as designing the analysis with specific principles. This webinar will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between data analysts. This will hopefully provide guidance for future work in characterizing the data analytic process.

Design Principles of Data Analysis

The data revolution has sparked greater interest in data analysis practices. While much attention has been given to statistical thinking, another type of complementary thinking that appears in data analysis is design thinking – a problem-solving approach focused on understanding the intended users of a product. When facing a problem, differences arise in how data analysts construct data analyses, including choices in methods, tools, and workflows. These choices impact the analysis outputs and user experience. Therefore, a data analyst’s role can be seen as designing the analysis with specific principles. This webinar will introduce six design principles for data analysis and describe how they can be mapped to data analyses in a quantitative and informative manner. We also provide empirical evidence of variation of these principles within and between data analysts. This will hopefully provide guidance for future work in characterizing the data analytic process.

Demystifying Invited Session Proposals

Invited sessions at conferences provide important opportunities for the exchange of ideas. But how do we get invited? And how can we do the inviting? In this panel, we bring together experienced women in statistics from all career stages to share their tips on organizing invited sessions. Our panelists have planned and participated in numerous successful invited sessions at statistical conferences and have served on program committees to plan and select these sessions on a large scale. This session, sponsored by the Caucus for Women in Statistics, is intended to demystify the invited session proposal process and to empower researchers to submit their ideas in the future.

Causal Quartet: When Statistics Alone Do Not Tell the Full Story

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. The second half of the talk will focus on how statistical techniques can be leveraged to examine the impact of a potential unmeasured confounder. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders, introducing the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

Estimating causal effects: this be madness, yet there is method in it

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. To adjust or not adjust, that is the question; we demonstrate that statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

A Visual Diagnostic Tool for Causal Inference

This talk covers several diagnostic plots that are helpful when attempting to answer a causal question. They can be used to visualize the target population, balance, and treatment effect heterogeneity.