On September 14, 2023, UFC boss Dana White joined the famous UK broadcaster Piers Morgan for an interview on Morgan's YouTube channel, Piers Morgan Uncensored ("Piers Morgan vs Dana White | The Full Interview"). Morgan began asking Dana questions about his parents and upbringing, a topic that Dana usually avoids. As the questions became more personal, Dana politely set clear boundaries by shying away from certain details. For example, Dana disclosed that his parents recently passed away, but avoided providing specific dates or details. This is unlike the UFC boss who, having done many press conferences, is trained in and comfortable with sharing precise numbers and facts. The topic was exhausted when Dana remarked that the questions reminded Morgan of his own wife. Dana White's reluctance to provide precise information about his relationship with his own parents demonstrates a below-average example of the Specificity attribute.
Oleksandr Usyk's walkout in his 2024 fight against Tyson Fury was one for the record books. Given the bright lights and costume, it's no wonder he felt ready to fight. He also won the fight.
In season 1, episode 4 of the hit Netflix show called Wednesday (2022), teenager Wednesday Addams is discussing plans for going to a school dance with a certain someone. In typical Wednesday fashion, she redirected the conversation from her feelings to her priorities. This was highlighted when Wednesday said that it's not her fault for being unable to interpret "emotional morse code." Her lack of interest in discussing feelings and emotions and word choices reflect the Emotion attribute.
In the hit Marvel cinematic universe movie Doctor Strange (2016), talented Neurosurgeon Doctor Stephen Strange suffers a tragic car accident which ruins his medical career. After finding and joining a group of witches and sorcerers, he learns that Kamar-Taj, the group's compound, is under attack. A former member turned rogue named Kaecilius is the attacker. He confronts Doctor Strange on a long stairway. When Kaecilius says "How long have you been at Kamar-Taj, Mister ...," Strange answers with his formal title, "Doctor." A confused Kaecilius replies with "Mister Doctor," and Strange quips back: "It's Strange." Still confused, Kaecilius replies with "Maybe, but who am I to judge." In this brief exchange, Doctor Strange's simple responses cause confusion over his name, and this lack of clarity demonstrates a well below-average example of the Readability attribute.
In the hit TV show Westworld (2016–2022), Dolores has a plan for getting what she wants. As the first self-aware AI host in the park, she knows how to control the action and convince others to do her bidding.
An ultra low attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra low attribute score would be lower than 95 of them and higher than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Very Low
5–10% percentile
A very low attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very low attribute score would be higher than five of them and lower than 90 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Low
10–20% percentile
A low attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a low attribute score would be higher than ten of them and lower than 80 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Slightly Low
20–40% percentile
A slightly low attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly low attribute score would be higher than 20 of them and lower than 60 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Average
40–60% percentile
An average attribute score is typical and represents 20% of the entire population. In a room with 100 other people, a person with an average attribute score would be higher than 40 of them and lower than 40 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Slightly High
60–80% percentile
A slightly high attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly high attribute score would be higher than 60 of them and lower than 20 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
High
80–90% percentile
A high attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a high attribute score would be higher than 80 of them and lower than 10 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Very High
90–95% percentile
A very high attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very high attribute score would be higher than 90 of them and lower than five of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.
Ultra High
95–100% percentile
An ultra high attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra high attribute score would be higher than 95 of them and lower than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.