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  2. Numeric precision in Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Numeric_precision_in...

    Here the 'IEEE 754 double value' resulting of the 15 bit figure is 3.330560653658221E-15, which is rounded by Excel for the 'user interface' to 15 digits 3.33056065365822E-15, and then displayed with 30 decimals digits gets one 'fake zero' added, thus the 'binary' and 'decimal' values in the sample are identical only in display, the values ...

  3. Numerical error - Wikipedia

    en.wikipedia.org/wiki/Numerical_error

    Time series of the Tent map for the parameter m=2.0 which shows numerical error: "the plot of time series (plot of x variable with respect to number of iterations) stops fluctuating and no values are observed after n=50". Parameter m= 2.0, initial point is random.

  4. Error bar - Wikipedia

    en.wikipedia.org/wiki/Error_bar

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

  5. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    There is not much faith in the accuracy of the value because the most uncertainty in any floating-point number is the digits on the far right. For example, 1.99999 × 10 2 − 1.99998 × 10 2 = 0.00001 × 10 2 = 1 × 10 − 5 × 10 2 = 1 × 10 − 3 {\displaystyle 1.99999\times 10^{2}-1.99998\times 10^{2}=0.00001\times 10^{2}=1\times 10^{-5 ...

  6. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    Production of a small p-value by multiple testing. 30 samples of 10 dots of random color (blue or red) are observed. On each sample, a two-tailed binomial test of the null hypothesis that blue and red are equally probable is performed. The first row shows the possible p-values as a function of the number of blue and red dots in the sample.

  7. Family-wise error rate - Wikipedia

    en.wikipedia.org/wiki/Family-wise_error_rate

    is the number of true null hypotheses, an unknown parameter; is the number of true alternative hypotheses; V is the number of false positives (Type I error) (also called "false discoveries") S is the number of true positives (also called "true discoveries") T is the number of false negatives (Type II error)

  8. Bessel's correction - Wikipedia

    en.wikipedia.org/wiki/Bessel's_correction

    The sum of the a 2-column and the b 2-column must be bigger than the sum within entries of the a 2-column, since all the entries within the b 2-column are positive (except when the population mean is the same as the sample mean, in which case all of the numbers in the last column will be 0). Therefore:

  9. Observational error - Wikipedia

    en.wikipedia.org/wiki/Observational_error

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