Running head: P-VALUES VS. CRITICAL VALUES Statistics: P Values vs. Critical Values Student’s First name, Middle initial and Last name Name of University Course Name April 30, 2011 The critical value method sets a finite value corresponding to a given significance level…
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However, if the calculated value is less than the critical value, the null hypothesis is accepted and the alternative hypothesis is rejected. The advantage of this method is that it helps define the rejection region in terms of the sample mean and therefore if the same experiment is repeated many times in the field, one can draw the conclusion of the hypothesis test right then without having to make any other calculations (Statistics Glossary, para 22). The disadvantage of this method is that one is stuck with a fixed level for the test. The probability value or p-value method is a measure of how likely the sample results are, assuming the null hypothesis is true. Small p-values suggest that the null hypothesis is unlikely to be true. The smaller the p-value, the more convincing is the rejection of the null hypothesis. The advantage of this method is that, by reporting the p-value one allows the reader to decide if the result is significant or not (Statistics Glossary, para 26). For e.g., one might think that the 5% level of significance is good while the reader may prefer 2% level of significance. Therefore, the p-value allows the reader to draw their own conclusions. The disadvantage of this method is that one can’t make quick conclusions based on just the sample values as can be done in the critical value method.
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