There are two broad cases: either you know the distribution or you don't.
Distribution known: Procedures are known for many, many situations. In some cases, it's possible to transform given data to fit available procedures. In other situations, it might be necessary to create new procedures.
Distribution unknown: Often a procedure that was developed for known distributions has been shown to work for distributions that are only similar to the known distributions. Recourse may alternatively be had to the so-called nonparametric statistics, that make minimal assumptions about distributions.
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Standard deviation of 0 can only be attained if all observations are identical. That is, the variable in question has just one possible value so statistical considerations are irrelevant.
An empty space or interval in a set of data refers to a range of values within which no data points exist. This absence can indicate gaps in measurement, missing data, or natural boundaries in the data distribution. Identifying these intervals is crucial for understanding the overall structure and completeness of the dataset, as they may affect statistical analyses or interpretations.
No. The fact that the outcome of one trial does not affect the outcome of any other trial follows from the fact that the trials that are independent. Whether the distribution is binomial or not is totally irrelevant.
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Do to unequal distribution of electronic cloud between two atoms, the both atoms acquire a partial positive atoms, the both atoms acquire a partial positive and negative charge resperctively.