BS Summary: This article contains 30 faulty reasoning types, including Negativity Bias, Availability Heuristic, and Burden of Proof, with Appeal to Authority as the most egregious example at 16% saturation with 98 hits. Analysis detected 1,055 faulty-reasoning hits from 611 analyzed words, generating a BS Score of 46.5% and a BS Rank of 43% (12,579 of 21,887 articles). This article is better (less manipulative) than 57.50% of the article peer group.

Secretary of the State Stephanie Thomas saying Connecticut elections are safe. 
Four days after President Donald Trump fanned doubts about the security and accuracy of American elections, Connecticut Democrats offered a well-practiced rebuttal in a press conference at the Old State House in Hartford. 
“The president used a prime time address to tell Americans that voting systems can be manipulated and elections can be stolen. 
Here in Connecticut, that is just plain wrong,” Secretary of the State Stephanie Thomas said. 
“It comes down to three things: people, paper, and proof.” 
Thomas was joined by a half-dozen others, including Gov. 
Ned Lamont, Lt. 
Gov. 
Susan Bysiewicz, Attorney General William Tong, Rep. 
Matt Blumenthal, a member of the League of Women Voters and a UConn professor. 
“Elections do not run themselves,” Thomas said. 
“They aren’t run by some faceless bureaucrats in Washington. 
They’re run in every single Connecticut town by registrars, town clerks, moderators, poll workers  people from both parties, people who live in the communities they serve.” 
She noted that votes are cast on paper ballots, tabulated by scanners that are not connected to the internet as a guard against hacking. 
The “proof” she referenced at the post-election audits conducted at UConn, overseen by a computer science professor, Alexander Russell. 
Russell said those audits never have found a significant discrepancy in the original count and the tally produced during an audit when the ballots from randomly selected polling places are scanned again. 
“So you don’t have to choose between trusting a politician and trusting a machine. 
You can actually look at the proof,” Thomas said. 
In a televised speech Thursday night, the president suggested that U.S. elections are vulnerable to fraud. 
The White House called it “a bombshell.” 
“If you look at voting today, it’s in such bad shape in so many states,” Trump said. 
“And we are committing to fix it, and we are also committing to be working with those states and local jurisdictions to help them fix and patch known technical vulnerabilities before the midterm elections.” 
But he offered no evidence that votes were changed due to those supposed vulnerabilities. 
Attorney General William Tong spoke at a press conference about election security in Hartford on July 20, 2026. 
Credit: Julia Levine / CT Mirror 
The next day, a federal judge in Connecticut dismissed a Trump administration lawsuit that sought access to Connecticut’s full voter registration data, including confidential information such as driver’s license numbers and Social Security numbers. 
In her decision, U.S. 
District Judge Kari A. 
Dooley found that the Department of Justice had no authority under the Civil Rights Act of 1960 to require Connecticut to hand over its voter registration list, as the list was created by the Secretary of the State and not covered under the federal law. 
Tong said in a statement, “This lawsuit was an illegal attempt to disenfranchise Connecticut voters, and today, the court shut it down. 
The Constitution is clear: the president does not control our elections and has no right to manipulate Connecticut voter rolls. 
 We will not be bullied into handing over sensitive voter information just because Trump demands it.” 
The dispute began in January when the DOJ’s Civil Rights Division filed a lawsuit against Connecticut after Thomas declined to provide the DOJ with all of the voter registration information that was requested. 
She said Connecticut law prohibited her office from disclosing driver’s license numbers and Social Security numbers to the public or government agencies. 
The lawsuit was one of 22 that the DOJ filed against other states and the District of Columbia. 
Some have been dismissed; others are ongoing. 
Article reasoning-pattern comparisonThis article: 8.7%Mark Pazniokas: 2.9%CT Mirror: 2.6%Confirmation Bias8.7%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.6%Anchoring Bias0.0%This article: 11.0%Mark Pazniokas: 1.7%CT Mirror: 3.0%Availability Heuristic11.0%This article: 5.6%Mark Pazniokas: 1.2%CT Mirror: 1.0%Representativeness Heuristic5.6%This article: 5.6%Mark Pazniokas: 0.4%CT Mirror: 0.3%Hindsight Bias5.6%This article: 7.4%Mark Pazniokas: 1.4%CT Mirror: 1.3%Overconfidence Bias7.4%This article: 3.4%Mark Pazniokas: 8.2%CT Mirror: 5.3%Framing Effect3.4%This article: 0.0%Mark Pazniokas: 0.2%CT Mirror: 0.7%Loss Aversion0.0%This article: 4.4%Mark Pazniokas: 0.3%CT Mirror: 0.6%Status Quo Bias4.4%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.3%Sunk Cost Effect0.0%This article: 7.4%Mark Pazniokas: 1.7%CT Mirror: 3.1%Optimism Bias7.4%This article: 0.0%Mark Pazniokas: 0.8%CT Mirror: 1.9%Pessimism Bias0.0%This article: 12.8%Mark Pazniokas: 7.5%CT Mirror: 7.1%Negativity Bias12.8%This article: 3.6%Mark Pazniokas: 4.5%CT Mirror: 2.1%Self-Serving Bias3.6%This article: 5.4%Mark Pazniokas: 0.9%CT Mirror: 0.5%Fundamental Attribution Error5.4%This article: 0.0%Mark Pazniokas: 0.4%CT Mirror: 0.1%Actor-Observer Bias0.0%This article: 1.5%Mark Pazniokas: 0.9%CT Mirror: 1.0%In-Group Bias1.5%This article: 0.0%Mark Pazniokas: 0.3%CT Mirror: 0.2%Out-Group Homogeneity Bias0.0%This article: 3.1%Mark Pazniokas: 5.3%CT Mirror: 1.2%Halo Effect3.1%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.0%Horn Effect0.0%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.0%Dunning-Kruger Effect0.0%This article: 6.5%Mark Pazniokas: 0.8%CT Mirror: 0.8%Recency Bias6.5%This article: 0.0%Mark Pazniokas: 0.3%CT Mirror: 0.4%Primacy Effect0.0%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.0%Blind-Spot Bias0.0%This article: 5.1%Mark Pazniokas: 2.1%CT Mirror: 1.0%Ad Hominem5.1%This article: 3.4%Mark Pazniokas: 0.6%CT Mirror: 0.3%Straw Man3.4%This article: 16.0%Mark Pazniokas: 4.0%CT Mirror: 3.5%Appeal to Authority16.0%This article: 2.3%Mark Pazniokas: 0.8%CT Mirror: 1.5%False Dilemma2.3%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.8%Slippery Slope0.0%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.1%Circular Reasoning0.0%This article: 8.0%Mark Pazniokas: 3.7%CT Mirror: 4.0%Hasty Generalization8.0%This article: 0.0%Mark Pazniokas: 0.8%CT Mirror: 0.1%Red Herring0.0%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.7%Bandwagon0.0%This article: 3.9%Mark Pazniokas: 5.0%CT Mirror: 5.2%Appeal to Emotion3.9%This article: 5.7%Mark Pazniokas: 1.7%CT Mirror: 0.8%Begging the Question5.7%This article: 10.8%Mark Pazniokas: 1.0%CT Mirror: 2.1%Post Hoc (False Cause)10.8%This article: 2.8%Mark Pazniokas: 0.2%CT Mirror: 0.2%Tu Quoque2.8%This article: 11.0%Mark Pazniokas: 2.2%CT Mirror: 0.7%Burden of Proof11.0%This article: 3.9%Mark Pazniokas: 0.3%CT Mirror: 0.1%Appeal to Nature3.9%This article: 0.0%Mark Pazniokas: 0.3%CT Mirror: 0.2%Composition/Division0.0%This article: 0.0%Mark Pazniokas: 1.2%CT Mirror: 2.7%Anecdotal0.0%This article: 0.0%Mark Pazniokas: 0.3%CT Mirror: 0.1%No True Scotsman0.0%This article: 1.5%Mark Pazniokas: 3.2%CT Mirror: 1.8%Ambiguity (Equivocation)1.5%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.0%Gambler’s Fallacy0.0%This article: 1.6%Mark Pazniokas: 0.5%CT Mirror: 0.2%Middle Ground1.6%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.0%Personal Incredulity0.0%This article: 0.0%Mark Pazniokas: 0.4%CT Mirror: 0.2%Special Pleading0.0%This article: 0.0%Mark Pazniokas: 0.0%CT Mirror: 0.2%Genetic Fallacy0.0%This article: 5.4%Mark Pazniokas: 1.4%CT Mirror: 0.8%Unattributed Quote5.4%This article: 3.4%Mark Pazniokas: 2.2%CT Mirror: 1.1%Quote-first Misdirection3.4%This article: 1.5%Mark Pazniokas: 2.4%CT Mirror: 2.8%Biased Writer Voice1.5%This article: 0.0%Mark Pazniokas: 0.9%CT Mirror: 2.7%Indoctrination0.0%This article: 0.0%Mark Pazniokas: 0.6%CT Mirror: 1.1%Politically Left Leaning Bias0.0%This article: 0.0%Mark Pazniokas: 1.0%CT Mirror: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Mark Pazniokas: 0.6%CT Mirror: 0.6%Attempt to Sell a Product or S…0.0%

611 words analyzed.

Speakers

6speakers62%attributed speech230writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageStephanie Thomas • 11 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageStephanie Thomas • 21 words • 100.0% coverageStephanie Thomas • 15 words • 0.0% coverageStephanie Thomas • 10 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageStephanie Thomas • 7 words • 0.0% coverageStephanie Thomas • 9 words • 100.0% coverageStephanie Thomas • 27 words • 0.0% coverageStephanie Thomas • 24 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageAlexander Russell • 32 words • 0.0% coverageStephanie Thomas • 14 words • 0.0% coverageStephanie Thomas • 9 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWhite House • 7 words • 0.0% coverageDonald Trump • 17 words • 0.0% coverageDonald Trump • 34 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWilliam Tong • 18 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 34 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageKari A. Dooley • 45 words • 0.0% coverageWilliam Tong • 22 words • 100.0% coverageWilliam Tong • 20 words • 0.0% coverageWilliam Tong • 17 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageStephanie Thomas • 22 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverage
Selected voice

William Tong

77%flagged-word coverage
77 attributed words20% of attributed speech69% writer coverage
0%15.0%30.0%Unattributed Quote+28.6 ptsWriter: 0.0%William Tong: 28.6%28.6%

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.