The purpose of hypothesis testing is not to question the computed value of the sample statistic but, to make an estimate about the difference between that sample statistic and a hypothesized population parameter. Now, the next step is to decide what criterion should be used for deciding whether to accept or reject the null hypothesis.
If we test a hypothesis at the 5 per cent level of significance, this means that we will reject the null hypothesis if the difference between the sample statistics and the hypothesized population parameter is very large.
Assuming the hypothesis to be correct, then, the significance level indicates the percentage of sample means that is outside certain limits.
If we plot a figure of significance test, it interprets the significance level. In each tail, 2.5 percent of the area is located. We can see that 95 per cent of all the area under the curve is included in an interval extending 1.96 sx on either side of the hypothesized mean. Thus, there is no significant difference between the sample statistics and the hypothesized population parameter, which is 95 per cent of the area. In the remaining 5 per cent, a significant difference does exist.
95 per cent of the area under the curve is the one where, we accept the null hypothesis. The two small parts under the curve, representing a total of 5 per cent of the area, are those where, we reject the null hypothesis.