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  2. Standard normal table - Wikipedia

    en.wikipedia.org/wiki/Standard_normal_table

    To find a negative value such as -0.83, one could use a cumulative table for negative z-values [3] which yield a probability of 0.20327. But since the normal distribution curve is symmetrical, probabilities for only positive values of Z are typically given.

  3. Standard score - Wikipedia

    en.wikipedia.org/wiki/Standard_score

    Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.

  4. p-value - Wikipedia

    en.wikipedia.org/wiki/P-value

    In null-hypothesis significance testing, the p-value [note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2] [3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.

  5. Z-test - Wikipedia

    en.wikipedia.org/wiki/Z-test

    The two-sided p-value is approximately 0.014 (twice the one-sided p-value). Another way of stating things is that with probability 1 − 0.014 = 0.986, a simple random sample of 55 students would have a mean test score within 4 units of the population mean.

  6. Normal curve equivalent - Wikipedia

    en.wikipedia.org/wiki/Normal_curve_equivalent

    where z is the standard score or "z-score", i.e. z is how many standard deviations above the mean the raw score is (z is negative if the raw score is below the mean). The reason for the choice of the number 21.06 is to bring about the following result: If the scores are normally distributed (i.e. they follow the "bell-shaped curve") then

  7. One- and two-tailed tests - Wikipedia

    en.wikipedia.org/wiki/One-_and_two-tailed_tests

    The p-value was introduced by Karl Pearson [6] in the Pearson's chi-squared test, where he defined P (original notation) as the probability that the statistic would be at or above a given level. This is a one-tailed definition, and the chi-squared distribution is asymmetric, only assuming positive or zero values, and has only one tail, the ...

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  9. 97.5th percentile point - Wikipedia

    en.wikipedia.org/wiki/97.5th_percentile_point

    "The value for which P = .05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation is to be considered significant or not." [11] In Table 1 of the same work, he gave the more precise value 1.959964. [12] In 1970, the value truncated to 20 decimal places was calculated to be