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  2. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    Consider the previous example with men's heights and suppose we have a random sample of n people. The sample mean could serve as a good estimator of the population mean. Then we have: The difference between the height of each man in the sample and the unobservable population mean is a statistical error, whereas

  3. Probable error - Wikipedia

    en.wikipedia.org/wiki/Probable_error

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  4. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

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  5. Experimental uncertainty analysis - Wikipedia

    en.wikipedia.org/wiki/Experimental_uncertainty...

    Essentially, the mean is the location of the PDF on the real number line, and the variance is a description of the scatter or dispersion or width of the PDF. To illustrate, Figure 1 shows the so-called Normal PDF , which will be assumed to be the distribution of the observed time periods in the pendulum experiment.

  6. William Sealy Gosset - Wikipedia

    en.wikipedia.org/wiki/William_Sealy_Gosset

    He pioneered small sample experimental design and analysis with an economic approach to the logic of uncertainty. Gosset published under the pen name Student and developed most famously Student's t-distribution – originally called Student's "z" – and "Student's test of statistical significance ".

  7. Probability of error - Wikipedia

    en.wikipedia.org/wiki/Probability_of_error

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  8. Sampling error - Wikipedia

    en.wikipedia.org/wiki/Sampling_error

    Upload file; Search. Search ... a truly random sample means selecting individuals from a population with an equivalent probability; ... which can dramatically ...

  9. Bessel's correction - Wikipedia

    en.wikipedia.org/wiki/Bessel's_correction

    In estimating the population variance from a sample when the population mean is unknown, the uncorrected sample variance is the mean of the squares of deviations of sample values from the sample mean (i.e., using a multiplicative factor 1/n). In this case, the sample variance is a biased estimator of the population variance. Multiplying the ...