P-Value and hypothesis test
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Hypothesis test are setup to determine the validity of a statistical claim. Every hypothesis test contains two sets of opposing statements/hypothesis:
Null Hypothesis
Alternative Hypothesis
The null hypothesis states that the population parameter is equal to the claimed value. For example, if the claim is that average time to cook pizza is 5 minutes, the notation for null hypothesis would be:
You need to define an opposing statement/hypothesis, in cases where null hypothesis fails. There can be three possibilities for an alternative hypothesis.
The population parameter is not equal to the claimed value
The population parameter is greater than the claimed value
The population parameter is less than the claimed value
P-value is used as the cutoff point for rejecting the null hypothesis. A greater p-value means there is stronger evidence in the favor of the null hypothesis.
In a statistical hypothesis test, p-value is the level of marginal significance representing a given event's probability of occurrence.
The p-value is a number between 0 and 1 and interpreted in the following way:
A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis.
A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.
p-values very close to the cutoff (0.05) are considered to be marginal (could go either way).
Note: Always report the p-value so your readers can draw their own conclusions.
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