PSY 315 wk 2

Five Steps of Hypothesis Testing

In order to test a hypothesis the researchers requires restating their research problems as on alternative and null hypothesis. The alternative hypothesis as stated above is : OCD symptoms in population 1 children who take a new herbal medication (NHM) will be reduced more than population 2 children who do not take the new herbal medication (NHM). The null hypothesis is the opposite of the alternative hypothesis as stated above is: OCD symptoms in population 1 and 2 children who take a new herbal medication (NHM) will not experience a reduction in their symptoms. The alternative and null hypotheses are important operations in testing the hypothesis. In the beginning of this test the hypothesis explains the two populations being studied in the steps of hypothesis testing(Aron, Aron & Coups, 2006). In the final steps of testing the hypothesis, each population is compared to each other in the research to determine if the alternative hypothesis will be accepted verses the null hypothesis or the population has no difference.

The second step of the hypothesis testing is the most crucial step. This involves the comparison distribution. This graph will represent the two groups or population studied in the hypothesis test and the null hypothesis. The graphical representations will show the results from the research and allows the researchers to identify and understand the data in the population that the alternative hypothesis predicts will change if accepted in the last step of the research. The graph will also show the results for the null hypothesis if accepted in the last step of the hypothesis testing. Other steps left for testing the hypothesis are calculating the mean and standard deviation for the second population.

The third step in hypothesis testing is just as important as the second step. In this step we need to figure out the level of significance of our predicted alternative hypothesis. In the research and calculations there needs to be a significant level of change, like 0.05. This will mean there is a chance that you will accept the alternative hypothesis when the null hypothesis is actually true. If the significance level is lower, the greater the burned of proof you will need to refuse the null hypothesis or accept the alterative hypothesis.

The fourth step is using the z-score equation to determine the sample distribution: population one that represents our alternative hypothesis (Aron, Aron & Coups, 2006). The equation is worked out as following: Z=(X-M)/SD (Aron, Aron & Coups, 2006). This equation is used in the final step of testing the hypothesis to see if the alternative or null hypothesis will be accepted.

The fifth step to hypothesis testing is drawing a conclusion. From testing both hypotheses is not set up so that an individual can absolutely prove a null hypothesis. Although, when you do not have evidence that proves the null hypothesis wrong, then you fail to reject the null hypothesis. However, when there is strong enough evidence against the null hypothesis then that is when you reject the null hypothesis. After the results of a hypothesis test and presenting the information, make sure you are descriptive with the statistics in your conclusion. Also make sure that your conclusion translates into a statement about the alternative hypothesis.