Probability is "a number expressing the likelihood of occurrence of a specific event" (Shao, 1994, p. 217). For use in inferential statistics, this probability must be statistically independent (Peebles, 2003).

The central limit theorem is relevant to probability analysis, and it is especially relevant to the use of probability in business. The central limit theorem holds that the totals (and therefore the means) of random samples will be normally distributed no matter what the distribution in the population is like, provided only that the samples are large enough. In most instances where inferential statistics are applied in hypothesis testing, population distributions are unknown. Problems in hypothesis testing related to the central limit theorem most often occur when the sample data apply to perceptions and subjective evaluations by individuals, as opposed to objective data (Peebles, 2003).

In classical statistical analysis, probability is predicated on the condition that the outcomes of an experiment are equally likely to occur. The approach in classical statistical analysis to probability is that the lack of knowledge implies that all possibilities are equally likely. The classical conception of probability applies when the events have the same chance of occurring and the set of events are mutually exclusive and collectively exhaustive. This approach allows business researchers to project future outcomes with some degre

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