Paths Explored, Paths Omitted, Paths Obscured: Decision Points & Selective Reporting in End-to-End Data Analysis
Yang Liu, Tim Althoff, Jeffrey Heer
Abstract
Drawing reliable inferences from data involves many, sometimes arbitrary, decisions across phases of data collection, wrangling, and modeling. As different choices can lead to diverging conclusions, understanding how researchers make analytic decisions is important for supporting robust and replicable analysis. In this study, we pore over nine published research studies and conduct semi-structured interviews with their authors. We observe that researchers often base their decisions on methodological or theoretical concerns, but subject to constraints arising from the data, expertise, or perceived interpretability. We confirm that researchers may experiment with choices in search of desirable results, but also identify other reasons why researchers explore alternatives yet omit findings. In concert with our interviews, we also contribute visualizations for communicating decision processes throughout an analysis. Based on our results, we identify design opportunities for strengthening end-to-end analysis, for instance via tracking and meta-analysis of multiple decision paths.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext ff878e2a-4e29-4e5c-bbb4-be068e17a0f6Cited by top-tier papers23
- Boba: Authoring and Visualizing Multiverse AnalysesYang Liu, Alex Kale, Tim Althoff, Jeffrey HeerIEEE VIS 2020 · 79 citations
- A Critical Reflection on Visualization Research: Where Do Decision Making Tasks Hide?Evanthia Dimara, John T. StaskoIEEE VIS 2021 · 56 citations
- Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task DecompositionMajeed Kazemitabaar, Jack Williams, Ian Drosos, Tovi Grossman et al.UIST 2024 · 49 citations
- How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz StudyKen Gu, Madeleine Grunde-McLaughlin, Andrew M. McNutt, Jeffrey Heer et al.CHI 2024 · 37 citations
- How Do Analysts Understand and Verify AI-Assisted Data Analyses?Ken Gu, Ruoxi Shang, Tim Althoff, Chenglong Wang et al.CHI 2024 · 36 citations
Related papers
- Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential StatisticsEric Newburger, Niklas ElmqvistIEEE VIS 2023 · 7 citations
- Here's what you need to know about my data: Exploring Expert Knowledge's Role in Data AnalysisHaihan Lin, Maxim Lisnic, Derya Akbaba, Miriah Meyer et al.IEEE VIS 2025 · 1 citation
- Guidelines For Pursuing and Revealing Data AbstractionsAlex Bigelow, Katy Williams, Katherine E. IsaacsIEEE VIS 2020 · 12 citations
- Odds and Insights: Decision Quality in Exploratory Data Analysis Under UncertaintyAbhraneel Sarma, Xiaoying Pu, Yuan Cui, Michael Correll et al.CHI 2024 · 8 citations
- A Qualitative Study on the Implementation Design Decisions of DevelopersJenny T. Liang, Maryam Arab, Minhyuk Ko, Amy J. Ko et al.ICSE 2023 · 11 citations
