We have done it several times in the past. The objective of hypothesis testing is to assess, using sample data, two mutually exclusive theories about the properties of a population. Please see my earlier post for the definitions of sample and population. The two theories are the null hypothesis and the alternative hypothesis.
The null hypothesis (H0) typically represents the default state or the state of “no effect“. For example, you compare the means of two groups, such as people who took a particular drug and people who received the placebo. As a drug researcher, your objective is to find the effectiveness of the medicine. And that lays the foundation for your alternative hypothesis (HA or H1) – that the drug has a non-zero effect. The default state (H0) assumes the drug has no impact. To be specific, H0 assumes the difference between two means equals zero.
H1 states that the population parameter value does not equal the H0 value. Notice the words, population and parameter. The ambition of the test is to create statements on the who space and not just on the sample itself. And if the sample contains sufficient evidence, we will see what is sufficient, you will reject the null hypothesis in favour of the alternative.
