Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home Statistical Tests Fundamendals Interpreting p values  
See also: level of significance, hypothesis testing  
Interpreting p valuesFor statistical tests there is one value which specifies the strength
of its evidence:
A low pvalue for a statistical test should lead to the rejection of the null hypothesis. Thus, it is important to know the null and the alternative hypotheses. Pvalues provide a sense of the strength of the evidence against
the null hypothesis.
An example should clarify the point: Suppose you have to decide whether there are any differences in the wear and tear of truck tires between two different brands. The null hypothesis will be that the wear does not differ, the alternative hypothesis is that they do differ. Assuming that the data (18 samples each) is normally distributed and the means (2.03 and 2.69 mm) and standard deviations (1.30, and 1.11, respectively) are known, we can calculate the test statistic t=1.762.
Using the tdistribution, we can find the corresponding pvalue of 0.086. This means that in 86 out of 1000 cases, the test statistic will exceed the value of 1.762, although the null hypothesis is true. Or, in other words, if you reject the null hypothesis, you commit a mistake in 8.6 percent of the cases.


Home Statistical Tests Fundamendals Interpreting p values 