A hypothesis test evaluates two mutually exclusive statements about a population to determine which statement is best supported by the sample data. These two statements are called the null hypothesis and alternative hypothesis (Frost, 2019). A study typically has both a null and alternative hypothesis that needs testing to determine which of the two is significant to the identified problem.
The first example of how research uses hypothesis testing is that of a study that wants to prove that new fathers that have received the same level of education and training regarding their child’s care are more likely to be engaging and willing to take part in the birth process and aftercare of the newborn. The survey reveals that about 232 people out of 500 showed signs of willingness to participate in these processes.
Therefore, the study has to create a null and alternative hypothesis first with the former neither being for or against this claim and the latter supporting it. The next step would be to collect data and summarize it into test statistics followed by determining the p-value. P value is the probability that you would obtain the effect observed in your sample, or larger, if the null hypothesis is true for the populations. P values are calculated based on the sample data and the assumption that the null hypothesis is true (Frost, 2019).
Let’s say the p-value, in this case, is 0.0118 meaning that it is less than 0.05 indicating that the alternative hypothesis was right (“S.3.2 Hypothesis Testing”, 2019). The second example is that of a study that wants to measure the difference in the impact of dieting or exercising in the amount of weight loss noted in participants over six months.
The null hypothesis, in this case, will claim that no significant difference will present itself while the alternative hypothesis will suggest that there will exist some variance between the two methods. Let’s say the p-value is 0.06, which is more than 0.05 meaning that the null hypothesis was right. For a null hypothesis to be rejected, the p-value has to be less than 0.05, which is the cutoff mark for significance (“Hypothesis Testing,” 2017).
As a postpartum nurse, it is extremely important for me to be using methods and practices that are both clinically and statistically significant. Therefore, knowing how to distinguish between a null an alternative hypothesis and how to use the p-value to support or reject either one of them ensures that I provide my patients with the best care possible.
Reference:
Frost (2019). How Hypothesis Tests Work: Significance Levels (Alpha) and P values. Retrieved from https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-significance-levels-alpha-p-values
Hypothesis Testing: Upper-, Lower, and Two-Tailed Tests. (2017). Boston University School of Public Health. Retrieved from http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTest-Means-Proportions/BS704_HypothesisTest-Means-Proportions3.html
S.3.2 Hypothesis Testing (P-Value Approach). (2019). Penn State..Retrieved from https://onlinecourses.science.psu.edu/statprogram/…