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  2. Year 1900 problem - Wikipedia

    en.wikipedia.org/wiki/Year_1900_problem

    February 29, 1900 in the Polish-language version of Microsoft Excel for Microsoft Windows. The year 1900 problem concerns the misinterpretation of years recorded by only their last two digits, and whether they occurred before or after the year 1900.

  3. Errors and residuals - Wikipedia

    en.wikipedia.org/wiki/Errors_and_residuals

    For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is −0.05 meters. The expected value, being the mean of the entire population, is typically unobservable, and hence ...

  4. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    To reduce the probability of committing a type I error, making the alpha value more stringent is both simple and efficient. To decrease the probability of committing a type II error, which is closely associated with analyses' power, either increasing the test's sample size or relaxing the alpha level could increase the analyses' power.

  5. F-test - Wikipedia

    en.wikipedia.org/wiki/F-test

    To locate the critical F value in the F table, one needs to utilize the respective degrees of freedom. This involves identifying the appropriate row and column in the F table that corresponds to the significance level being tested (e.g., 5%). [6] How to use critical F values: If the F statistic < the critical F value Fail to reject null hypothesis

  6. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    For a value that is sampled with an unbiased normally distributed error, the above depicts the proportion of samples that would fall between 0, 1, 2, and 3 standard deviations above and below the actual value.

  7. Propagation of uncertainty - Wikipedia

    en.wikipedia.org/wiki/Propagation_of_uncertainty

    For example, the 68% confidence limits for a one-dimensional variable belonging to a normal distribution are approximately ± one standard deviation σ from the central value x, which means that the region x ± σ will cover the true value in roughly 68% of cases. If the uncertainties are correlated then covariance must be taken into account ...

  8. Observational error - Wikipedia

    en.wikipedia.org/wiki/Observational_error

    This page was last edited on 17 February 2025, at 21:02 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  9. Mean absolute error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_error

    Quantity disagreement is the absolute value of the mean error: [4] | = |. Allocation disagreement is MAE minus quantity disagreement. It is also possible to identify the types of difference by looking at an (,) plot. Quantity difference exists when the average of the X values does not equal the average of the Y values.