You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 simpson_episodes
【Schema】
# Table: Credit
[
(episode_id:TEXT, Examples: [S20-E10, S20-E13, S20-E14]),
(category:TEXT, Examples: [Casting Department, Animation Department, Sound Department]),
(person:TEXT, Examples: [Bonita Pietila, Adam Kuhlman, Carlton Batten]),
(role:TEXT, Examples: [casting, additional timer, adr engineer]),
(credited:TEXT, Examples: [true, false])
]
# Table: Award
[
(award_id:INTEGER, Primary Key, Examples: [325, 326, 327]),
(organization:TEXT, Examples: [Primetime Emmy Awards]),
(year:INTEGER, Examples: [2009, 2010, 2017]),
(award_category:TEXT, Examples: [Primetime Emmy, Annie, Image Award]),
(award:TEXT),
(person:TEXT, Examples: [Dan Castellaneta, Hank Azaria, Harry Shearer]),
(role:TEXT, Examples: [executive producer]),
(episode_id:TEXT, Examples: [S20-E18, S20-E16, S20-E8]),
(season:TEXT),
(song:TEXT),
(result:TEXT, Examples: [Winner, Nominee])
]
# Table: Person
[
(name:TEXT, Primary Key, Examples: [Adam Greeley, Adam Klein, Adam Kuhlman]),
(birthdate:TEXT, Examples: [1963-05-04, 1975-03-30, 1969-12-04]),
(birth_name:TEXT, Examples: [Marc Edward Wilmore, Valentina Lantigua, Joseph Stewart Burns]),
(birth_place:TEXT, Examples: [USA, Brooklyn, Chicago]),
(birth_region:TEXT, Examples: [California, New York, Illinois]),
(birth_country:TEXT, Examples: [USA, Canada, Ireland]),
(height_meters:REAL, Examples: [1.85, 1.75, 2.03]),
(nickname:TEXT, Examples: [Jim, Doofus, Hoops])
]
# Table: Vote
[
(episode_id:TEXT, Examples: [S20-E1, S20-E10, S20-E11]),
(stars:INTEGER, Examples: [2, 3, 4]),
(votes:INTEGER, Examples: [16, 20, 36]),
(percent:REAL, Examples: [1.3, 1.7, 3.0])
]
# Table: Character_Award
[
(award_id:INTEGER, Examples: [325, 326, 327]),
(character:TEXT, Examples: [Homer Simpson, Moe Szyslak, Kent Brockman])
]
# Table: Episode
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(season:INTEGER, Examples: [20]),
(episode:INTEGER, Examples: [1, 2, 3]),
(number_in_series:INTEGER, Examples: [421, 422, 423]),
(title:TEXT, Examples: [Sex, Pies and Idiot Scrapes]),
(summary:TEXT),
(air_date:TEXT, Examples: [2008-09-28, 2008-10-05, 2008-10-19]),
(episode_image:TEXT),
(rating:REAL, Examples: [7.2, 7.0, 7.1]),
(votes:INTEGER, Examples: [1192, 1055, 1015])
]
# Table: Keyword
[
(episode_id:TEXT, Primary Key, Examples: [S20-E1, S20-E10, S20-E11]),
(keyword:TEXT, Primary Key, Examples: [1930s to 2020s, erotic bakery, cake])
]
【Foreign keys】
Credit.episode_id=Episode.episode_id
Credit.person=Person.name
Award.person=Person.name
Award.episode_id=Episode.episode_id
Vote.episode_id=Episode.episode_id
Character_Award.award_id=Award.award_id
Keyword.episode_id=Episode.episode_id

    【Question】
    Among the episodes aired in 2008 with votes ranges from 920 to 950, list their percent. aired in 2008 refers to air_date LIKE '2008%'; votes ranges from 920 to 950 refers to votes BETWEEN 920 AND 950

    【Evidence】
    aired in 2008 refers to air_date LIKE '2008%'; votes ranges from 920 to 950 refers to votes BETWEEN 920 AND 950

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T2.percent FROM Episode AS T1 INNER JOIN Vote AS T2 ON T2.episode_id = T1.episode_id WHERE SUBSTR(T1.air_date, 1, 4) = '2008' AND T1.votes BETWEEN 950 AND 960;