However, if the mean of sample is not likely to be significantly greater than 2% (and remain at say around %), then we CANNOT reject the null hypothesis. The challenge comes on how to decide on such close range cases. To make a conclusion from selected samples and results, a level of significance is to be determined, which enables a conclusion to be made about the null hypothesis. The alternative hypothesis enables establishing the level of significance or the "critical value” concept for deciding on such close range cases. As per the standard definition , “A critical value is a cutoff value that defines the boundaries beyond which less than 5% of sample means can be obtained if the null hypothesis is true. Sample means obtained beyond a critical value will result in a decision to reject the null hypothesis”. In the above example, if we have defined the critical value as %, and the calculated mean comes to %, then we reject the null hypothesis. A critical value establishes a clear demarcation about acceptance or rejection.