# P-Value and hypothesis test

## Hypothesis test

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

### Null 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:

### Alternative Hypothesis

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 valueThe population parameter is

*greater than*the claimed valueThe population parameter is

*less than*the claimed value

## P-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.*

Link: https://www.dummies.com/education/math/statistics/what-a-p-value-tells-you-about-statistical-data/

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