MAGA candidate Paxton defeats Senate stalwart Cornyn in Texas primary ⁠78%

By Matthew Choi⁠0%

5/26/2026, 9:00:00 AM

BS Summary: This article contains 7 faulty reasoning types, including Negativity Bias, Biased Writer Voice, and Availability Heuristic, with Framing Effect as the most egregious example at 69.6% saturation with 55 hits. Analysis detected 345 faulty-reasoning hits from 79 analyzed words, generating a BS Score of 69.6% and a BS Rank of ⁠78% (4,851 of 21,886 articles). This article is worse (more manipulative) than 77.80% of the article peer group.

Four-term Sen. 
John Cornyn (R-Texas), who once came close to becoming majority leader in the Senate, lost a primary challenge to Texas Attorney General Ken Paxton on Tuesday, ending a bitter race driven by a MAGA insurgency that ballooned into the most expensive Senate primary in history. 
Republican Senate candidate Ken Paxton speaks to supporters at a campaign stop on May 15 in Little Elm, Texas. 
(Ron Jenkins/Getty Images) 
Article reasoning-pattern comparisonThis article: 0.0%Matthew Choi: 3.3%The Washington Post: 3.8%Confirmation Bias0.0%This article: 0.0%Matthew Choi: 5.2%The Washington Post: 1.3%Anchoring Bias0.0%This article: 57.0%Matthew Choi: 9.4%The Washington Post: 4.9%Availability Heuristic57.0%This article: 0.0%Matthew Choi: 2.0%The Washington Post: 1.0%Representativeness Heuristic0.0%This article: 0.0%Matthew Choi: 2.2%The Washington Post: 0.5%Hindsight Bias0.0%This article: 0.0%Matthew Choi: 0.9%The Washington Post: 1.2%Overconfidence Bias0.0%This article: 69.6%Matthew Choi: 29.4%The Washington Post: 21.5%Framing Effect69.6%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.8%Loss Aversion0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.9%Status Quo Bias0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Sunk Cost Effect0.0%This article: 0.0%Matthew Choi: 1.6%The Washington Post: 3.6%Optimism Bias0.0%This article: 0.0%Matthew Choi: 3.5%The Washington Post: 3.6%Pessimism Bias0.0%This article: 69.6%Matthew Choi: 18.8%The Washington Post: 18.5%Negativity Bias69.6%This article: 0.0%Matthew Choi: 1.5%The Washington Post: 1.5%Self-Serving Bias0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.9%Fundamental Attribution Error0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Actor-Observer Bias0.0%This article: 0.0%Matthew Choi: 3.7%The Washington Post: 2.2%In-Group Bias0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.8%Out-Group Homogeneity Bias0.0%This article: 0.0%Matthew Choi: 4.7%The Washington Post: 2.1%Halo Effect0.0%This article: 0.0%Matthew Choi: 2.0%The Washington Post: 0.4%Horn Effect0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Matthew Choi: 1.2%The Washington Post: 2.4%Recency Bias0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.0%Primacy Effect0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.0%Blind-Spot Bias0.0%This article: 0.0%Matthew Choi: 2.0%The Washington Post: 0.9%Ad Hominem0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Straw Man0.0%This article: 0.0%Matthew Choi: 1.6%The Washington Post: 5.2%Appeal to Authority0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.5%False Dilemma0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.0%Slippery Slope0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Circular Reasoning0.0%This article: 0.0%Matthew Choi: 7.3%The Washington Post: 5.9%Hasty Generalization0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Red Herring0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.5%Bandwagon0.0%This article: 57.0%Matthew Choi: 6.1%The Washington Post: 6.3%Appeal to Emotion57.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.0%Begging the Question0.0%This article: 0.0%Matthew Choi: 1.2%The Washington Post: 4.5%Post Hoc (False Cause)0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.2%Tu Quoque0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.6%Burden of Proof0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Appeal to Nature0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Composition/Division0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.9%Anecdotal0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.2%No True Scotsman0.0%This article: 57.0%Matthew Choi: 3.0%The Washington Post: 2.7%Ambiguity (Equivocation)57.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Middle Ground0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.0%Personal Incredulity0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.1%Special Pleading0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 0.2%Genetic Fallacy0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 4.8%Unattributed Quote0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 2.5%Quote-first Misdirection0.0%This article: 69.6%Matthew Choi: 18.1%The Washington Post: 15.2%Biased Writer Voice69.6%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 2.0%Indoctrination0.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 3.6%Politically Left Leaning Bias0.0%This article: 57.0%Matthew Choi: 6.8%The Washington Post: 1.4%Politically Right Leaning Bias57.0%This article: 0.0%Matthew Choi: 0.0%The Washington Post: 1.5%Attempt to Sell a Product or S…0.0%

79 words analyzed.

Speakers

1speaker24%attributed speech60writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 10 words • 100.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 45 words • 100.0% coverageKen Paxton • 19 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverage
Selected voice

Ken Paxton

0%flagged-word coverage
19 attributed words100% of attributed speech92% writer coverage
0%47.5%95.0%Biased Writer Voice-91.7 ptsWriter: 91.7%Ken Paxton: 0.0%0.0%Politically Right Leaning -75.0 ptsWriter: 75.0%Ken Paxton: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.