ABSTRACT
In statistical testing, a result is called statistically significant if it is unlikely to have occurred by chance, and hence provides enough evidence to reject the hypothesis of 'no effect'. The tests involve comparing the observed values with theoretical values. The tests establish whether there is a relationship between the variables, or whether pure chance could produce the observed results. For most scientific research, a statistical significance test eliminates the possibility that the results arose by chance, allowing a rejection of the null hypothesis. Industrial applications of statistics are often concerned with making decisions about populations and population parameters. The assumptions made are called statistical hypotheses or just hypotheses and are usually concerned with statements about probability distributions of populations. Hypotheses may also be used when comparisons are being made. The null hypothesis indicates that a 3% defect rate is acceptable to the manufacturer.
