ls, researchers, and others with knowledge about events and relationships which reduce the level of uncertainty in the data upon which decisions are based, thereby improving the quality of decisions.
Inference is among the most important of the uses made of statistics. Hypothesis testing falls in the realm of inferential statistics. The basic theoretical principle upon which inferential statistics are based is probability. Probability is a number expressing the likelihood of occurrence of a specific event. For use in inferential statistics, this probability must be statistically independent.
In inferential statistical applications, probability is largely a function of sampling, as well as of relationships, because it is usually not feasible, or even possible, to measure an entire population. Thus, hypothesis testing is applied to sample data in inferential statistical applications. Such was the case in this study. It is with respect to samples and their characteristics, and their use in hypothesis testing, that the central theorem and central tendency measures are important. 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. Therefore, the central limit theorem assumes a high level of importance in hypothesis testing.
The principal techniques of statistical inference are interval estimation and hypothesis testing. Although statistical inference techniques are directly concerned with estimating values or with the testing of hypotheses concerning population parameters, the results of these procedures are related to alternative courses of action or to alternative decisions. As an example, the acceptance of a null hypothes...