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  2. Skill - Wikipedia

    en.wikipedia.org/wiki/Skill

    Some examples of general skills are time management, teamwork and leadership, and self-motivation. In contrast, domain-specific skills would be used only for a certain job, e.g. operating a sand blaster. Skill usually requires certain environmental stimuli and situations to assess the level of skill being shown and used.

  3. Marie Antoinette - Wikipedia

    en.wikipedia.org/wiki/Marie_Antoinette

    Early on 16 October, Marie Antoinette was declared guilty of the three main charges against her: depletion of the national treasury, conspiracy against the internal and external security of the State, and high treason because of her intelligence activities in the interest of the enemy; the latter charge alone was enough to condemn her to death ...

  4. Block letters - Wikipedia

    en.wikipedia.org/wiki/Block_letters

    The examples and perspective in this article may not represent a worldwide view of the subject. You may improve this article, discuss the issue on the talk page, or create a new article, as appropriate. (May 2017) (Learn how and when to remove this message)

  5. Nonformal learning - Wikipedia

    en.wikipedia.org/wiki/Nonformal_learning

    Examples of non-formal learning include swimming sessions for toddlers, community-based sports programs, and programs developed by organisations such as the Boy Scouts, the Girl Guides, community or non-credit adult education courses, sports or fitness programs, professional conference style seminars, and continuing professional development. [2]

  6. Statistical parameter - Wikipedia

    en.wikipedia.org/wiki/Statistical_parameter

    A "parameter" is to a population as a "statistic" is to a sample; that is to say, a parameter describes the true value calculated from the full population (such as the population mean), whereas a statistic is an estimated measurement of the parameter based on a sample (such as the sample mean).

  7. Bias (statistics) - Wikipedia

    en.wikipedia.org/wiki/Bias_(statistics)

    Spectrum bias arises from evaluating diagnostic tests on biased patient samples, leading to an overestimate of the sensitivity and specificity of the test. For example, a high prevalence of disease in a study population increases positive predictive values, which will cause a bias between the prediction values and the real ones. [4]

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