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# Statistically Significant

How do scientists test out different theories? One such strategy is using a p-value test.

## Problem Statement

On average, do dogs weigh more than cats?

## State the Hypothesis

This is where we make our guesses. When we try to prove that event A causes event B, we have to provide the burden of proof, just like a prosecutor in a court case. Now we state our hypotheses.

Null Hypothesis: On average, dogs do not weigh more than cats.
Alternate Hypothesis: On average, dogs weight more than cats.

## Set the Significance Level

For the test, we need to set a certain probability threshold that will indicate whether or not the probability we get after doing the test is significant or not. Generally the threshold is set at 0.05 or 5%. So if the test gives a probability less than 5%, then the information is significant enough to reject the null hypothesis. If the probability is greater than 5%, then the test is not significant enough to reject the null hypothesis.

Set the significance level or alpha to 0.05

## Perform the Test

After setting the significance level, we can now perform the statistical test. This can pretty much be any test like chi square test, t-test, z-score, etc. After performing the test, we get a probabilistic value based on the test. This is called the p-value. If the p-value is less than the significance level, then we reject the null hypothesis.

## Conclusion

Now we can conclude our test.

P-value = 0.03. Therefore I reject my null hypothesis.
P-value = 0.16. Therefore I do not reject my null hypothesis.