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Statistical reasoning is a theory that involves choosing and checking ingredients for a statistical problem. These ingredients include: data, a model, and a prior.
Statistical reasoning involves making interpretations based on sets of data, graphical representations, and statistical summaries. It can involve connecting one concept to another, such as understanding the relationship between the mean and standard deviation in a distribution. Statistical reasoning can also involve learning to use statistics and/or probabilistic re-zoning to make decisions in situations where knowledge is incomplete or the future unpredictable.
Some examples of statistical reasoning include: Probability using counting methods Conditional probability Permutations and factorial notation Modeling data with a line of best fit Mutually exclusive and non mutually-exclusive events Modeling data with a curve of best fit Independent events
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