A hypothesis test has the objective of testing different results against each other. You use them to check a result against something you already believe is true. In a hypothesis test, you’re checking if the new alternative hypothesis would challenge and replace the already existing null hypothesis .
Hypothesis tests are either one-sided or two-sided. In a one-sided test, the alternative hypothesis is left-sided with or right-sided with . In a two-sided test, the alternative hypothesis is . In all three cases, is the pre-existing probability of what you’re comparing, and is the probability you are going to find.
Note! In hypothesis testing, you calculate the alternative hypothesis to say something about the null hypothesis.
Rule
For example, you would have a reason to believe that a high observed value of , makes the alternative hypothesis seem reasonable.
Example 1
There is a drug on the market that you know cures of all patients. A company has come up with a new drug they believe is better than what is already on the market. This new drug has cured 92 of 103 patients in tests. Determine if the new drug is really better than the old one.
This is a classic case of hypothesis testing by binomial distribution. You now follow the recipe above to answer the task and select % level of significance since it is not a question of medication for a serious illness.
The alternative hypothesis in this case is that the new drug is better. The reason for this is that you only need to know if you are going to approve for sale and thus the new drug must be better:
so cannot be rejected. The new drug does not enter the market.
If the value had been less than the level of significance, that would mean that the new drug represented by the alternative hypothesis is better, and that you are sure of this with statistical significance.