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  2. 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.

  3. Standard normal table - Wikipedia

    en.wikipedia.org/wiki/Standard_normal_table

    Since probability tables cannot be printed for every normal distribution, as there are an infinite variety of normal distributions, it is common practice to convert a normal to a standard normal (known as a z-score) and then use the standard normal table to find probabilities. [2]

  4. 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

  5. Sten scores - Wikipedia

    en.wikipedia.org/wiki/Sten_scores

    It is also usually necessary to truncate such scores, particularly if the scores are skewed. An alternative method of calculation requires that the scale developer prepare a table to convert raw scores to sten scores by apportioning percentages according to the distribution shown in the table.

  6. Fisher transformation - Wikipedia

    en.wikipedia.org/wiki/Fisher_transformation

    The application of Fisher's transformation can be enhanced using a software calculator as shown in the figure. Assuming that the r-squared value found is 0.80, that there are 30 data [clarification needed], and accepting a 90% confidence interval, the r-squared value in another random sample from the same population may range from 0.656 to 0.888.

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  8. Z-test - Wikipedia

    en.wikipedia.org/wiki/Z-test

    To calculate the standardized statistic = (¯), we need to either know or have an approximate value for σ 2, from which we can calculate =. In some applications, σ 2 is known, but this is uncommon. If the sample size is moderate or large, we can substitute the sample variance for σ 2 , giving a plug-in test.

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