What Will It Take to Make Great Salt Lake Great Again? ⁠67%

4/13/2026, 1:59:46 PM

BS Summary: This article contains 17 faulty reasoning types, including Halo Effect, Pessimism Bias, and Recency Bias, with Appeal to Authority as the most egregious example at 41.2% saturation with 63 hits. Analysis detected 414 faulty-reasoning hits from 153 analyzed words, generating a BS Score of 60.7% and a BS Rank of ⁠67% (7,276 of 21,887 articles). This article is worse (more manipulative) than 66.80% of the article peer group.

Some observers may have balked when President Trump posted on social media earlier this year promising to “MAKE ‘THE LAKE’ GREAT AGAIN.” 
He backed it up in his budget proposal to Congress, requesting a billion dollars to ensure Great Salt Lake’s long-term sustainability. 
The challenge, says BYU ecologist Ben Abbott, will be turning that money into water that can actually help fill the lake. 
Abbott and biologist Bonnie Baxter join us to talk about how the lake is doing and where it’s headed after a dry winter, a warm spring, and an active legislative session. 
GUESTS 
Ben Abbott is an assistant professor of ecosystem ecology at Brigham Young University and executive director of Grow the Flow, an non-profit advocacy group. 
Bonnie Baxter is a professor of biology at Westminster College and director of the Great Salt Lake Institute. 
Broadcast April 16, 2026 
Article reasoning-pattern comparisonThis article: 13.7%KUER: 2.8%Confirmation Bias13.7%This article: 0.0%KUER: 1.3%Anchoring Bias0.0%This article: 14.4%KUER: 3.4%Availability Heuristic14.4%This article: 0.0%KUER: 1.2%Representativeness Heuristic0.0%This article: 0.0%KUER: 0.5%Hindsight Bias0.0%This article: 0.0%KUER: 1.9%Overconfidence Bias0.0%This article: 7.2%KUER: 7.4%Framing Effect7.2%This article: 0.0%KUER: 1.3%Loss Aversion0.0%This article: 7.2%KUER: 1.2%Status Quo Bias7.2%This article: 0.0%KUER: 0.3%Sunk Cost Effect0.0%This article: 0.0%KUER: 4.4%Optimism Bias0.0%This article: 20.3%KUER: 2.4%Pessimism Bias20.3%This article: 13.7%KUER: 6.3%Negativity Bias13.7%This article: 0.0%KUER: 2.2%Self-Serving Bias0.0%This article: 0.0%KUER: 0.9%Fundamental Attribution Error0.0%This article: 0.0%KUER: 0.2%Actor-Observer Bias0.0%This article: 0.0%KUER: 1.9%In-Group Bias0.0%This article: 0.0%KUER: 0.6%Out-Group Homogeneity Bias0.0%This article: 27.5%KUER: 2.3%Halo Effect27.5%This article: 0.0%KUER: 0.1%Horn Effect0.0%This article: 0.0%KUER: 0.0%Dunning-Kruger Effect0.0%This article: 20.3%KUER: 1.2%Recency Bias20.3%This article: 14.4%KUER: 0.3%Primacy Effect14.4%This article: 0.0%KUER: 0.1%Blind-Spot Bias0.0%This article: 0.0%KUER: 0.6%Ad Hominem0.0%This article: 0.0%KUER: 0.4%Straw Man0.0%This article: 41.2%KUER: 4.7%Appeal to Authority41.2%This article: 0.0%KUER: 1.7%False Dilemma0.0%This article: 0.0%KUER: 1.1%Slippery Slope0.0%This article: 0.0%KUER: 0.2%Circular Reasoning0.0%This article: 0.0%KUER: 4.1%Hasty Generalization0.0%This article: 0.0%KUER: 0.2%Red Herring0.0%This article: 0.0%KUER: 0.7%Bandwagon0.0%This article: 0.0%KUER: 5.6%Appeal to Emotion0.0%This article: 13.7%KUER: 0.7%Begging the Question13.7%This article: 20.3%KUER: 2.4%Post Hoc (False Cause)20.3%This article: 0.0%KUER: 0.1%Tu Quoque0.0%This article: 0.0%KUER: 0.4%Burden of Proof0.0%This article: 0.0%KUER: 0.2%Appeal to Nature0.0%This article: 0.0%KUER: 0.3%Composition/Division0.0%This article: 0.0%KUER: 3.1%Anecdotal0.0%This article: 0.0%KUER: 0.1%No True Scotsman0.0%This article: 7.2%KUER: 1.5%Ambiguity (Equivocation)7.2%This article: 0.0%KUER: 0.0%Gambler’s Fallacy0.0%This article: 0.0%KUER: 0.2%Middle Ground0.0%This article: 0.0%KUER: 0.1%Personal Incredulity0.0%This article: 0.0%KUER: 0.1%Special Pleading0.0%This article: 0.0%KUER: 0.2%Genetic Fallacy0.0%This article: 14.4%KUER: 0.8%Unattributed Quote14.4%This article: 14.4%KUER: 0.7%Quote-first Misdirection14.4%This article: 7.2%KUER: 2.2%Biased Writer Voice7.2%This article: 0.0%KUER: 1.6%Indoctrination0.0%This article: 0.0%KUER: 0.8%Politically Left Leaning Bias0.0%This article: 13.7%KUER: 0.3%Politically Right Leaning Bias13.7%This article: 0.0%KUER: 1.0%Attempt to Sell a Product or S…0.0%

153 words analyzed.

Speakers

1speaker14%attributed speech132writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 21 words • 100.0% coverageBen Abbott • 21 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverage
Selected voice

Ben Abbott

100%flagged-word coverage
21 attributed words100% of attributed speech96% writer coverage
0%10.0%20.0%Unattributed Quote-16.7 ptsWriter: 16.7%Ben Abbott: 0.0%0.0%Quote-first Misdirection-16.7 ptsWriter: 16.7%Ben Abbott: 0.0%0.0%Politically Right Leaning -15.9 ptsWriter: 15.9%Ben Abbott: 0.0%0.0%Biased Writer Voice-8.3 ptsWriter: 8.3%Ben Abbott: 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.