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On the structure of bad science matrices

Alex Albors, Hisham Bhatti, Lukshya Ganjoo, Raymond Guo, Dmitriy Kunisky, Rohan Mukherjee, Alicia Stepin and Tony Zeng

Vol. 19 (2026), No. 3, 443–460
Abstract

The bad science matrix problem consists in finding, among all matrices A n×n with rows having unit 2 norm, one that maximizes

β(A) = 1 2n x{1,1}nAx.

Our main contribution is an explicit construction of an n × n matrix A showing that β(A) log 2 (n + 1) which is only 18% smaller than the asymptotic rate. We prove that every entry of any optimal matrix is a square root of a rational number, and we find provably optimal matrices for n 4.

Keywords
discrepancy theory, combinatorics, bad science matrices
Mathematical Subject Classification
Primary: 68R05
Secondary: 52A20, 90C27
Milestones
Received: 7 August 2024
Revised: 3 December 2024
Accepted: 2 January 2025
Published: 12 June 2026

Communicated by Glenn Hurlbert
Authors
Alex Albors
University of Washington
Seattle, WA
United States
Hisham Bhatti
University of Washington
Seattle, WA
United States
Lukshya Ganjoo
University of Washington
Seattle, WA
United States
Raymond Guo
University of Washington
Seattle, WA
United States
Dmitriy Kunisky
Department of Applied Mathematics and Statistics
Johns Hopkins University
Baltimore, MD
United States
Rohan Mukherjee
University of Washington
Seattle, WA
United States
Alicia Stepin
University of Washington
Seattle, WA
United States
Tony Zeng
Department of Mathematics
University of Washington
Seattle, WA
United States