Steps involved in a statistical hypothesis test are to -

1. State the null and alternative hypothesis

2. Determine the size

3. Compute probability value

4. Decision making step (either accept or reject)

5. Draw a conclusion

Hypothesis testing is used to determine if a statement about the value of a population parameter should or should not be rejected.

To set up the null and alternative hypothesis we must consider the context of the situation so the conclusion provides the information that the decision maker wants - what is the purpose of collecting the sample, and what is the conclusion we are hoping to reach?

Can you elaborate based on the probability value (also called the p-value)?

The null hypothesis is a tentative assumption about the population parameter and the alternative hypothesis is a statement that is opposite of that. Either the null hypothesis is true, or the alternative, but not both.

The person responsible for the hypothesis test specifies the level of significance which is 'the probability of making a Type I error when the null hypothesis is true as an equality' ( Anderson, Sweeney, Williams, 2017, p.393). A type I error is the rejection of the null hypothesis when the null hypothesis is true.

An example: The average persons income in an organization is 100K.

The null and the alternative hypotheses would be. H0:= 100K, and Ha:100K