730089
Beschreibung
Karteikarten von lauramyates3, aktualisiert more than 1 year ago
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Erstellt von lauramyates3
vor etwa 10 Jahre
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| Frage | Antworten |
| Define Data | Raw facts and figures with no meaning by themselves |
| Define Information | Data with context which makes it meaningful that has been processed by a computer |
| Define Knowledge | Derived from information by applying rules to it. Using information to make decisions and get to a conclusion |
| Example to show relationship between data; information and knowledge | Swimming: Data: 80,70,65 Information: Swim time for the 100m front crawl Knowledge: 80,70,65 (seconds) Swimmer no.3 is the fastest swimmer in this race and thus wins |
| Benefits of encoding data | Takes up less memory/storage space on the hard drive e.g. M is 1 byte whereas male is 4 bytes. Faster to type in e.g. F is much faster to type in than Female Quicker/easier to validate single character e.g F or M less likely to make entry errors Faster for computer to search - F faster to find than Female |
| Give an example for Coarsening precision | When looking at eye colour in criminal database and only have blue and green options but eyes were bluey green then there are not enough categories making the data LESS USEFUL, giving misleading results |
| Three disadvantages of encoding data | Coarsening precision Reliance on value judgement in limited list leads to inconsistency Limited choice leading to loss of precision |
| Give an example for reliance on value judgement in limited list leads to inconsistency | Tall, Medium, Small rather than actual specific height of students or weight: fat, skinny, obese. One persons tall is medium for another person. |
| Give an example for limited choice leading to the loss of precision | Large or Medium or Small means data will be less PRECISE when considering actual height, better to have greater range to choose from: 1.2-1.4m, 1.4m-1.6m etc. |
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