There is no single statistic formula. As a professional statistician with 30+ years of experience I can tell you that there are at least hundreds.
The 12 in the Kruskal-Wallis formula arises from the need to scale the test statistic appropriately for the sample sizes involved. Specifically, it helps to normalize the sum of ranks used in the calculation, ensuring that the resulting test statistic follows a chi-squared distribution under the null hypothesis. This scaling factor accounts for the expected variance in the ranks, making the test more robust and allowing for proper statistical inference.
The formula for statistics can vary depending on the specific statistic being calculated. Common formulas include mean (average), median (middle value), mode (most frequent value), standard deviation (measure of dispersion), and correlation coefficient (relationship between variables). It is important to determine the appropriate formula based on the data and the statistical analysis being conducted.
statistic is singular and statistic is plural
When you formulate and test a statistical hypothesis, you compute a test statistic (a numerical value using a formula depending on the test). If the test statistic falls in the critical region, it leads us to reject our hypothesis. If it does not fall in the critical region, we do not reject our hypothesis. The critical region is a numerical interval.
Variance is a characteristic parameter of a probability distribution: it is not a statistic. In any particular situation (with a few strange exceptions) it has only one value and therefore cannot have any bias.
No, it is not. A descriptive statistic is a measure such as mean, standard deviation etc., computed from a set of observations. A p value is something that is obtained by computing a test statistic (using a formula which may involve mean, variance etc.,) and finding the probability of obtaining a value as great as or greater than the one actually obtained. In other words, a p value is a probability and must lie between 0 and 1 whereas a descriptive statistic is not a probability. It is just a number used to describe a specific characteristic of a set of sata.
a statistic that is not in youre favor
The Yates' correction for continuity is used to adjust the chi-square statistic for 2x2 contingency tables when sample sizes are small, helping to reduce the chance of Type I errors. The formula for the corrected chi-square statistic (χ²) is: [ χ² = \frac{(|O_1 - E_1| - 0.5)^2}{E_1} + \frac{(|O_2 - E_2| - 0.5)^2}{E_2} ] where (O_1) and (O_2) are the observed frequencies, and (E_1) and (E_2) are the expected frequencies for the respective categories. The subtraction of 0.5 accounts for the continuity correction.
The formula for standard deviation has both a square (which is a power of 2) and a square-root (a power of 1/2). Both must be there to balance each other, to keep the standard deviation value's magnitude similar to (having the same units as) the sample numbers from which it's calculated. If either is removed from the formula, the resulting standard deviation value will have different units, reducing its usefulness as a meaningful statistic.
It is a defensive statistic that stands for "Assists"
definations of statistic by different authors
estadísticas = statistics estadística = statistic