When investigating a result from hypothesis testing, the p-value will state how likely the hypothesis is. More specifically, in hypothesis testing, some event is tested on a sample data to see if the event actually has an effect. An example of event and sample data can be some drug applied on a group of rats. With the result in hand, one would like to know if the difference seen on the sample data was because of chance, or because the event actually had an effect, i.e. investigating if the drug is actually working. The way this is solved is by first creating a null hypothesis denotedH0which is the hypothesis stating that there isnoeffect. An alternative hypothesis H1 is
C.2. P-VALUE
also created, which is the hypothesis stating that thereisan effect. The p-value is defined as the probability, under the assumption thatH0is true, of obtaining a result equal or more extreme than what was actually observed.
Examples of test statistics calculating the p-value are: z-test [42], Student’s t-test [61], and the Kolomogorov-Smirnov test [46]. If the p-value is below some predefinedαvalue, usually 0.05, the null hypothesis can be rejected, hence the eventdidhave an effect on the group.
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