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In statistics, Cohen's h, popularized by Jacob Cohen, is a measure of distance between two proportions or probabilities. Cohen's h has several related uses: It can be used to describe the difference between two proportions as "small", "medium", or "large". It can be used to determine if the difference between two proportions is "meaningful".
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in
Horses are used to measure distances in horse racing – a horse length (shortened to merely a length when the context makes it obvious) equals roughly 8 feet or 2.4 metres. Shorter distances are measured in fractions of a horse length; also common are measurements of a full or fraction of a head, a neck, or a nose. [10]
109 km – length of High Speed 1 between London and the Channel Tunnel [159] 130 km – range of a Scud-A missile; 163 km – length of the Suez Canal; 164 km – length of the Danyang–Kunshan Grand Bridge; 213 km – length of Paris Métro; 217 km – length of the Grand Union Canal; 223 km – length of the Madrid Metro
Length is commonly understood to mean the most extended dimension of a fixed object. [1] However, this is not always the case and may depend on the position the object is in. Various terms for the length of a fixed object are used, and these include height, which is vertical length or vertical extent, width, breadth, and depth.
For comparing significance tests, a meaningful measure of efficiency can be defined based on the sample size required for the test to achieve a given task power. [14] Pitman efficiency [15] and Bahadur efficiency (or Hodges–Lehmann efficiency) [16] [17] [18] relate to the comparison of the performance of statistical hypothesis testing procedures.