WSVN54%

‘He just looked so mean’: Palm Bay woman credits security alert with preventing encounter with 7-foot gator outside home 85%

By Rubén Rosario55%

7/21/2026, 12:28:07 PM

BS Summary: This article contains 21 faulty reasoning types, including Post Hoc (False Cause), Availability Heuristic, and Confirmation Bias, with Negativity Bias as the most egregious example at 45.1% saturation with 173 hits. Analysis detected 1,052 faulty-reasoning hits from 384 analyzed words, generating a BS Score of 77.1% and a BS Rank of 85% (3,231 of 21,198 articles). This article is worse (more manipulative) than 84.80% of the article peer group.

PALM BAY, Fla. 
(WSVN)  A gator invader gave a big scare to a Florida family who found the large reptile stretched out on the front porch, but the homeowner said her security alert came just in time, thankfully avoiding a wild wake-up call. 
The unsettling overnight encounter between Sierra Wynn’s family and a 7-foot, 6-inch alligator went down Friday morning in Brevard County. 
Wynn said the unwelcome visitor was lounging next to the front door of her home off Malabar Road in Palm Bay, way too close for comfort. 
“I thought at one point he was gonna come through the window,” she said. 
Wynn said her home security camera alerted her and her loved ones to an animal in the yard just before 4 a.m. 
The footage shows the massive gator patrolling the family’s front porch. 
“His tail was bouncing against the door and bouncing against the house and stuff, and it was such deep guttural hiss and growl, and he just looked so mean, I thought he was going to come through the window,” said Wynn. 
With surveillance cameras still rolling, Wynn watched from inside her home as Palm Bay Police and the Florida Fish and Wildlife Conservation Commission worked to wrangle the uninvited guest. 
“We were scared, terrified. 
At one point, when he was getting kind of agitated, with the flashlight in his eye, he actually looked right up at me in his eyes, and he started opening up his mouth, and he was growling and hissing,” she said. 
Wynn says that alert may have made all the difference, because otherwise, they wouldn’t have known there was something waiting for them just steps away from their doorbell. 
“If we didn’t have the cameras and the features where we get notifications that animals are in your yard, you know, who knows what would have happened?” 
she said. 
“If I would have opened the garage to let the dog out, if I would have come through the front door, I wouldn’t have known.” 
Gators are no stranger to residents of the Sunshine State. 
Experts advise those who find themselves in Wynn’s situation to call police. 
They will contact FWC to safely remove the animal. 
Article reasoning-pattern comparisonThis article: 25.0%Rubén Rosario: 3.6%WSVN: 2.8%Confirmation Bias25.0%This article: 0.0%Rubén Rosario: 1.3%WSVN: 1.0%Anchoring Bias0.0%This article: 29.4%Rubén Rosario: 4.3%WSVN: 3.1%Availability Heuristic29.4%This article: 2.6%Rubén Rosario: 1.8%WSVN: 1.3%Representativeness Heuristic2.6%This article: 13.8%Rubén Rosario: 1.8%WSVN: 0.8%Hindsight Bias13.8%This article: 0.0%Rubén Rosario: 0.2%WSVN: 0.8%Overconfidence Bias0.0%This article: 15.6%Rubén Rosario: 8.1%WSVN: 7.0%Framing Effect15.6%This article: 0.0%Rubén Rosario: 1.0%WSVN: 0.5%Loss Aversion0.0%This article: 0.0%Rubén Rosario: 0.6%WSVN: 0.7%Status Quo Bias0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Sunk Cost Effect0.0%This article: 2.3%Rubén Rosario: 0.8%WSVN: 2.0%Optimism Bias2.3%This article: 10.7%Rubén Rosario: 3.5%WSVN: 2.5%Pessimism Bias10.7%This article: 45.1%Rubén Rosario: 12.9%WSVN: 12.5%Negativity Bias45.1%This article: 0.0%Rubén Rosario: 2.8%WSVN: 1.5%Self-Serving Bias0.0%This article: 0.0%Rubén Rosario: 1.1%WSVN: 1.9%Fundamental Attribution Error0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.4%Actor-Observer Bias0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 1.0%In-Group Bias0.0%This article: 0.0%Rubén Rosario: 0.5%WSVN: 0.4%Out-Group Homogeneity Bias0.0%This article: 7.6%Rubén Rosario: 1.1%WSVN: 1.1%Halo Effect7.6%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Horn Effect0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Rubén Rosario: 1.1%WSVN: 1.5%Recency Bias0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.1%Primacy Effect0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Blind-Spot Bias0.0%This article: 0.0%Rubén Rosario: 0.7%WSVN: 0.5%Ad Hominem0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Straw Man0.0%This article: 3.1%Rubén Rosario: 3.6%WSVN: 4.1%Appeal to Authority3.1%This article: 8.9%Rubén Rosario: 1.2%WSVN: 1.2%False Dilemma8.9%This article: 0.0%Rubén Rosario: 3.3%WSVN: 2.0%Slippery Slope0.0%This article: 0.0%Rubén Rosario: 0.1%WSVN: 0.0%Circular Reasoning0.0%This article: 2.6%Rubén Rosario: 2.2%WSVN: 2.5%Hasty Generalization2.6%This article: 0.0%Rubén Rosario: 0.5%WSVN: 0.4%Red Herring0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Bandwagon0.0%This article: 1.0%Rubén Rosario: 5.1%WSVN: 5.1%Appeal to Emotion1.0%This article: 0.0%Rubén Rosario: 0.5%WSVN: 0.4%Begging the Question0.0%This article: 36.5%Rubén Rosario: 4.4%WSVN: 3.1%Post Hoc (False Cause)36.5%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Tu Quoque0.0%This article: 7.3%Rubén Rosario: 0.6%WSVN: 0.8%Burden of Proof7.3%This article: 0.0%Rubén Rosario: 0.2%WSVN: 0.1%Appeal to Nature0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Composition/Division0.0%This article: 25.0%Rubén Rosario: 2.6%WSVN: 3.1%Anecdotal25.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%No True Scotsman0.0%This article: 3.1%Rubén Rosario: 1.1%WSVN: 1.0%Ambiguity (Equivocation)3.1%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Middle Ground0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Personal Incredulity0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Special Pleading0.0%This article: 0.0%Rubén Rosario: 0.0%WSVN: 0.0%Genetic Fallacy0.0%This article: 10.7%Rubén Rosario: 2.4%WSVN: 2.2%Unattributed Quote10.7%This article: 4.9%Rubén Rosario: 1.8%WSVN: 1.1%Quote-first Misdirection4.9%This article: 15.6%Rubén Rosario: 4.6%WSVN: 4.0%Biased Writer Voice15.6%This article: 3.1%Rubén Rosario: 0.4%WSVN: 0.6%Indoctrination3.1%This article: 0.0%Rubén Rosario: 0.5%WSVN: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Rubén Rosario: 0.1%WSVN: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Rubén Rosario: 0.5%WSVN: 0.4%Attempt to Sell a Product or S…0.0%

384 words analyzed.

Speakers

1speaker59%attributed speech156writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 19 words • 100.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 41 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageSierra Wynn • 26 words • 0.0% coverageSierra Wynn • 14 words • 0.0% coverageSierra Wynn • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageSierra Wynn • 41 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageSierra Wynn • 4 words • 0.0% coverageSierra Wynn • 41 words • 100.0% coverageSierra Wynn • 28 words • 0.0% coverageSierra Wynn • 27 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageSierra Wynn • 25 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 12 words • 100.0% coverageWriter's voice • 9 words • 0.0% coverage
Selected voice

Sierra Wynn

100%flagged-word coverage
228 attributed words100% of attributed speech97% writer coverage
0%20.0%40.0%Biased Writer Voice-38.5 ptsWriter: 38.5%Sierra Wynn: 0.0%0.0%Unattributed Quote+18.0 ptsWriter: 0.0%Sierra Wynn: 18.0%18.0%Quote-first Misdirection-12.2 ptsWriter: 12.2%Sierra Wynn: 0.0%0.0%Indoctrination-7.7 ptsWriter: 7.7%Sierra Wynn: 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.