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In inferential statistics, a range of plausible values for some unknown parameter, such as a population mean, defined as an interval with a lower bound and an upper bound. [2] The precise values of these bounds are calculated from a pre-determined confidence level, chosen by the researcher. The confidence level represents the frequency of ...
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How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word "low" or "high"). [1] For example see: Binary option ) While an attribute is often intuitive, the variable is the operationalized way in which the attribute is represented for further data processing .
A "parameter" is to a population as a "statistic" is to a sample; that is to say, a parameter describes the true value calculated from the full population (such as the population mean), whereas a statistic is an estimated measurement of the parameter based on a sample (such as the sample mean).
In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. [1] Colloquially, measures of central tendency are often called averages. The term central tendency dates from the late 1920s. [2] The most common measures of central tendency are the arithmetic mean, the median, and ...
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.
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In statistics, completeness is a property of a statistic computed on a sample dataset in relation to a parametric model of the dataset. It is opposed to the concept of an ancillary statistic. While an ancillary statistic contains no information about the model parameters, a complete statistic contains only information about the parameters, and ...