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- QUESTION
Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.
| Subject | Nursing | Pages | 2 | Style | APA |
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Answer
Hypothesis Testing in Healthcare
Statistical hypothesis testing, or confirmatory data analysis, is an inference method used in making decisions by use of experimental data of a set of random variables, making assumptions about a population parameter (Sedgwick, 2010). In healthcare, the process is important in evaluating the strength of evidence from a sample and provide reliable parameter extrapolation framework in order to understand the entire population.
One example of the use of hypothesis testing is in comparison of two groups. Specifically, when a new treatment or intervention is to be introduced (like in case of dealing with a disease or health condition), its effectiveness is tested in comparison with another. Therefore, a test group and control group are set up, where the test group is treated to the new intervention while the control group is not (mostly treated on placebo). The hypothesis is developed that the new treatment is more effective, based on the null hypothesis of no difference of effectiveness. In this case, the parameter of interest is compared between the two groups, an empirical hypothesis.
The other example is in ascertaining a theory, like high use of sugar-sweetened beverages (SSBs) leads to obesity. In this case, measures of sugar intake and weight are taken from each subject and their correlation tested. The hypothesis is that there is a significant positive correlation, tested on the null hypothesis that there is no correlation between the two variables.
Though it has been criticized for being arbitrary and insufficient, most biological research studies use p-values to test significance of hypotheses (practicality of which is assured by the use of effect sizes), as posited by Ellis (2010). When the p-value is less than the alpha value or significance level (probability of rejecting a true null hypothesis), the null hypothesis is rejected, and vice-versa. For example, at 95% confidence level, the alpha is 5% (1-0.95).
As a clinician, need for surety is overly consequential in practice. Hypothesis testing is a chance for developing confidence on certain health parameters based on empirical evidence, which can be replicated and are dependable. This kind of confidence enhances patient interactions since nobody wants assumptive practices without surety on them. Cordiality of interactions enhance the patient recovery process.
References
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Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. Sedgwick, P. (2010). Statistical hypothesis testing. Bmj, 340, c2059.
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