lated research, the acceptance of a null hypothesis or the inability to reject a null hypothesis in statistical inference analysis which indicated that the sales level for a product will be below the breakeven point for that product would, also, typically be associated with a decision not to market the product.
Essentially, the choice between probability levels of Type I (Alpha) and Type II (Beta) errors is the basis on which the relative importance of two alternative types of mistakes are assessed in hypothesis testing in statistical inference analysis. In such analysis, decision procedures are based on the analysis of data collected through a random sampling of the total population.
The quality of any statistical analysis can be only as good as is the quality of the data upon which it is based. Thus, the reliability and validity of data collected for use in statistical analysis is of paramount importance.
Validity refers to the extent to which data or a data collection instrument measures what it is actually desired to measure. Reliability refers to the
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