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The five-step processes for hypothesis testing are the following. Step1. Specify the null hypothesis H0 and alternative hypothesis H1. The null hypothesis is the hypothesis that the researcher formulates and proceeds to test. If the null hypothesis is rejected after the test, the hypothesis to be accepted is called the alternative hypothesis. For example if the researcher wants to compare the average value generated by two different procedures the null hypothesis to be tested is [pic] and the alternative hypothesis is [pic] Step2.

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Specify the significance level ((). The significance level of a test is the probability of rejecting a null hypothesis when it is true. A test is to be constructed in such a manner that the probability of committing this error (Type I error) a pre assigned value. Step3. Specify the test statistic and its sampling distribution. The decision to accept or reject a null hypothesis is to be based on the sample values. But, the sample values as such cannot be used for this purpose.

Hence, we use a statistic, called test statistic, for this purpose.

Based on the value of this test statistic we decide either to reject or accept the null hypothesis. Step4. The fourth step is to calculate the probability value (often called the p value). The p-value is the probability of obtaining the test value as large as the observed value, when the null hypothesis was really true. Thus it can be interpreted as the probability of wrongly rejecting the null hypothesis. This is to be calculated by using the sampling distribution of the test statistic.

Step5. The p value computed in Step 4 is compared with the significance level chosen in Step 2. If the p value is less than or equal to the significance level, then the null hypothesis is rejected; if the probability is greater than the significance level then the null hypothesis is not rejected. When the null hypothesis is rejected, the outcome is said to be “statistically significant”. When the null hypothesis is not rejected then the outcome is said to be “not statistically significant. “

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