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Significance level is the criterion used in rejecting and accepting of the null hypothesis (Craft, J

#### Significance level is the criterion used in rejecting and accepting of the null hypothesis (Craft, J

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Significance level is the criterion used in rejecting and accepting of the null hypothesis (Craft, J.L. 1990). Normally, 95% id used as the significance level in many statistical tests. In this case, the significance level is said to be .05 (also referred to as “alpha”). According to Gore, S.M. & Altman, D.G. (1992), a test statistic is calculated and is then compared to the table value (value read from statistical tables). If the test statistic lies in the acceptance region, then the null is accepted and if in the critical region, the alternative is accepted and the null rejected.

According to Diamond, I. & Jefferies, J. (2001), a sampling distribution is a probability distribution from which all the measurements of certain populations are based. the mean, standard deviation, and the variance. While calculating the test statistics, an assumption about the population from which the data is derived from is made. This assumption cannot be made in the absence of a distribution. For example, the normal distribution is assumed in many cases since this allows the data to be standardized so that some  .The p-value is also called the significance value (Newton, R.R., & Rudestam, K.E. 1999). The p-value forms the basis of whether the null hypothesis is to be rejected or accepted (Diamond, I. & Jefferies, J. 2001). The p-value is compared with the significance level (the alpha) to determine whether rejection or acceptance. If p>.alpha, then the null hypothesis is accepted, and if p<.alpha, then the null hypothesis is rejected (Hinton, P.R. 1995).

In a one-tailed test, the aspect of equality in the alternative hypothesis does not exist (Belle, G. 2002). For example, if two samples are collected and have the means µ1 and µ2, then the null hypothesis can be H0: µ1 = µ2 Vs. H1: µ1 >. µ2 or H1: µ1 <. µ2. Note that .H1 has both <.>. signs and not = or ≠ signs.